import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
df = pd.read_csv("PEP1.csv")
pd.set_option('display.max_columns',None)
df.head()
| Id | MSSubClass | MSZoning | LotFrontage | LotArea | Street | Alley | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | OverallQual | OverallCond | YearBuilt | YearRemodAdd | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | MasVnrArea | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinSF1 | BsmtFinType2 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | Heating | HeatingQC | CentralAir | Electrical | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | FullBath | HalfBath | Bedroom | Kitchen | KitchenQual | TotRmsAbvGrd | Functional | Fireplaces | FireplaceQu | GarageType | GarageYrBlt | GarageFinish | GarageCars | GarageArea | GarageQual | GarageCond | PavedDrive | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | PoolQC | Fence | MiscFeature | MiscVal | MoSold | YrSold | SaleType | SaleCondition | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 60 | RL | 65.0 | 8450 | Pave | NaN | Reg | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | 7 | 5 | 2003 | 2003 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 196.0 | Gd | TA | PConc | Gd | TA | No | GLQ | 706 | Unf | 0 | 150 | 856 | GasA | Ex | Y | SBrkr | 856 | 854 | 0 | 1710 | 1 | 0 | 2 | 1 | 3 | 1 | Gd | 8 | Typ | 0 | NaN | Attchd | 2003.0 | RFn | 2 | 548 | TA | TA | Y | 0 | 61 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 2 | 2008 | WD | Normal | 208500 |
| 1 | 2 | 20 | RL | 80.0 | 9600 | Pave | NaN | Reg | Lvl | AllPub | FR2 | Gtl | Veenker | Feedr | Norm | 1Fam | 1Story | 6 | 8 | 1976 | 1976 | Gable | CompShg | MetalSd | MetalSd | None | 0.0 | TA | TA | CBlock | Gd | TA | Gd | ALQ | 978 | Unf | 0 | 284 | 1262 | GasA | Ex | Y | SBrkr | 1262 | 0 | 0 | 1262 | 0 | 1 | 2 | 0 | 3 | 1 | TA | 6 | Typ | 1 | TA | Attchd | 1976.0 | RFn | 2 | 460 | TA | TA | Y | 298 | 0 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 5 | 2007 | WD | Normal | 181500 |
| 2 | 3 | 60 | RL | 68.0 | 11250 | Pave | NaN | IR1 | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | 7 | 5 | 2001 | 2002 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 162.0 | Gd | TA | PConc | Gd | TA | Mn | GLQ | 486 | Unf | 0 | 434 | 920 | GasA | Ex | Y | SBrkr | 920 | 866 | 0 | 1786 | 1 | 0 | 2 | 1 | 3 | 1 | Gd | 6 | Typ | 1 | TA | Attchd | 2001.0 | RFn | 2 | 608 | TA | TA | Y | 0 | 42 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 9 | 2008 | WD | Normal | 223500 |
| 3 | 4 | 70 | RL | 60.0 | 9550 | Pave | NaN | IR1 | Lvl | AllPub | Corner | Gtl | Crawfor | Norm | Norm | 1Fam | 2Story | 7 | 5 | 1915 | 1970 | Gable | CompShg | Wd Sdng | Wd Shng | None | 0.0 | TA | TA | BrkTil | TA | Gd | No | ALQ | 216 | Unf | 0 | 540 | 756 | GasA | Gd | Y | SBrkr | 961 | 756 | 0 | 1717 | 1 | 0 | 1 | 0 | 3 | 1 | Gd | 7 | Typ | 1 | Gd | Detchd | 1998.0 | Unf | 3 | 642 | TA | TA | Y | 0 | 35 | 272 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 2 | 2006 | WD | Abnorml | 140000 |
| 4 | 5 | 60 | RL | 84.0 | 14260 | Pave | NaN | IR1 | Lvl | AllPub | FR2 | Gtl | NoRidge | Norm | Norm | 1Fam | 2Story | 8 | 5 | 2000 | 2000 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 350.0 | Gd | TA | PConc | Gd | TA | Av | GLQ | 655 | Unf | 0 | 490 | 1145 | GasA | Ex | Y | SBrkr | 1145 | 1053 | 0 | 2198 | 1 | 0 | 2 | 1 | 4 | 1 | Gd | 9 | Typ | 1 | TA | Attchd | 2000.0 | RFn | 3 | 836 | TA | TA | Y | 192 | 84 | 0 | 0 | 0 | 0 | NaN | NaN | NaN | 0 | 12 | 2008 | WD | Normal | 250000 |
df.describe(include="all")
| Id | MSSubClass | MSZoning | LotFrontage | LotArea | Street | Alley | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | OverallQual | OverallCond | YearBuilt | YearRemodAdd | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | MasVnrArea | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinSF1 | BsmtFinType2 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | Heating | HeatingQC | CentralAir | Electrical | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | FullBath | HalfBath | Bedroom | Kitchen | KitchenQual | TotRmsAbvGrd | Functional | Fireplaces | FireplaceQu | GarageType | GarageYrBlt | GarageFinish | GarageCars | GarageArea | GarageQual | GarageCond | PavedDrive | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | PoolQC | Fence | MiscFeature | MiscVal | MoSold | YrSold | SaleType | SaleCondition | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1460.000000 | 1460.000000 | 1460 | 1201.000000 | 1460.000000 | 1460 | 91 | 1460 | 1460 | 1460 | 1460 | 1460 | 1460 | 1460 | 1460 | 1460 | 1460 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460 | 1460 | 1460 | 1460 | 1452 | 1452.000000 | 1460 | 1460 | 1460 | 1423 | 1423 | 1422 | 1423 | 1460.000000 | 1422 | 1460.000000 | 1460.000000 | 1460.000000 | 1460 | 1460 | 1460 | 1459 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460 | 1460.000000 | 1460 | 1460.000000 | 770 | 1379 | 1379.000000 | 1379 | 1460.000000 | 1460.000000 | 1379 | 1379 | 1460 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 7 | 281 | 54 | 1460.000000 | 1460.000000 | 1460.000000 | 1460 | 1460 | 1460.000000 |
| unique | NaN | NaN | 5 | NaN | NaN | 2 | 2 | 4 | 4 | 2 | 5 | 3 | 25 | 9 | 8 | 5 | 8 | NaN | NaN | NaN | NaN | 6 | 8 | 15 | 16 | 4 | NaN | 4 | 5 | 6 | 4 | 4 | 4 | 6 | NaN | 6 | NaN | NaN | NaN | 6 | 5 | 2 | 5 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 4 | NaN | 7 | NaN | 5 | 6 | NaN | 3 | NaN | NaN | 5 | 5 | 3 | NaN | NaN | NaN | NaN | NaN | NaN | 3 | 4 | 4 | NaN | NaN | NaN | 9 | 6 | NaN |
| top | NaN | NaN | RL | NaN | NaN | Pave | Grvl | Reg | Lvl | AllPub | Inside | Gtl | mes | Norm | Norm | 1Fam | 1Story | NaN | NaN | NaN | NaN | Gable | CompShg | VinylSd | VinylSd | None | NaN | TA | TA | PConc | TA | TA | No | Unf | NaN | Unf | NaN | NaN | NaN | GasA | Ex | Y | SBrkr | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | TA | NaN | Typ | NaN | Gd | Attchd | NaN | Unf | NaN | NaN | TA | TA | Y | NaN | NaN | NaN | NaN | NaN | NaN | Gd | MnPrv | Shed | NaN | NaN | NaN | WD | Normal | NaN |
| freq | NaN | NaN | 1151 | NaN | NaN | 1454 | 50 | 925 | 1311 | 1459 | 1052 | 1382 | 225 | 1260 | 1445 | 1220 | 726 | NaN | NaN | NaN | NaN | 1141 | 1434 | 515 | 504 | 864 | NaN | 906 | 1282 | 647 | 649 | 1311 | 953 | 430 | NaN | 1256 | NaN | NaN | NaN | 1428 | 741 | 1365 | 1334 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 735 | NaN | 1360 | NaN | 380 | 870 | NaN | 605 | NaN | NaN | 1311 | 1326 | 1340 | NaN | NaN | NaN | NaN | NaN | NaN | 3 | 157 | 49 | NaN | NaN | NaN | 1267 | 1198 | NaN |
| mean | 730.500000 | 56.897260 | NaN | 70.049958 | 10516.828082 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6.099315 | 5.575342 | 1971.267808 | 1984.865753 | NaN | NaN | NaN | NaN | NaN | 103.685262 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 443.639726 | NaN | 46.549315 | 567.240411 | 1057.429452 | NaN | NaN | NaN | NaN | 1162.626712 | 346.992466 | 5.844521 | 1515.463699 | 0.425342 | 0.057534 | 1.565068 | 0.382877 | 2.866438 | 1.046575 | NaN | 6.517808 | NaN | 0.613014 | NaN | NaN | 1978.506164 | NaN | 1.767123 | 472.980137 | NaN | NaN | NaN | 94.244521 | 46.660274 | 21.954110 | 3.409589 | 15.060959 | 2.758904 | NaN | NaN | NaN | 43.489041 | 6.321918 | 2007.815753 | NaN | NaN | 180921.195890 |
| std | 421.610009 | 42.300571 | NaN | 24.284752 | 9981.264932 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.382997 | 1.112799 | 30.202904 | 20.645407 | NaN | NaN | NaN | NaN | NaN | 181.066207 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 456.098091 | NaN | 161.319273 | 441.866955 | 438.705324 | NaN | NaN | NaN | NaN | 386.587738 | 436.528436 | 48.623081 | 525.480383 | 0.518911 | 0.238753 | 0.550916 | 0.502885 | 0.815778 | 0.220338 | NaN | 1.625393 | NaN | 0.644666 | NaN | NaN | 24.689725 | NaN | 0.747315 | 213.804841 | NaN | NaN | NaN | 125.338794 | 66.256028 | 61.119149 | 29.317331 | 55.757415 | 40.177307 | NaN | NaN | NaN | 496.123024 | 2.703626 | 1.328095 | NaN | NaN | 79442.502883 |
| min | 1.000000 | 20.000000 | NaN | 21.000000 | 1300.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 1.000000 | 1.000000 | 1872.000000 | 1950.000000 | NaN | NaN | NaN | NaN | NaN | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.000000 | NaN | 0.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | NaN | 334.000000 | 0.000000 | 0.000000 | 334.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | 2.000000 | NaN | 0.000000 | NaN | NaN | 1900.000000 | NaN | 0.000000 | 0.000000 | NaN | NaN | NaN | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | 0.000000 | 1.000000 | 2006.000000 | NaN | NaN | 34900.000000 |
| 25% | 365.750000 | 20.000000 | NaN | 59.000000 | 7553.500000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5.000000 | 5.000000 | 1954.000000 | 1967.000000 | NaN | NaN | NaN | NaN | NaN | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.000000 | NaN | 0.000000 | 223.000000 | 795.750000 | NaN | NaN | NaN | NaN | 882.000000 | 0.000000 | 0.000000 | 1129.500000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 2.000000 | 1.000000 | NaN | 5.000000 | NaN | 0.000000 | NaN | NaN | 1961.000000 | NaN | 1.000000 | 334.500000 | NaN | NaN | NaN | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | 0.000000 | 5.000000 | 2007.000000 | NaN | NaN | 129975.000000 |
| 50% | 730.500000 | 50.000000 | NaN | 69.000000 | 9478.500000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 6.000000 | 5.000000 | 1973.000000 | 1994.000000 | NaN | NaN | NaN | NaN | NaN | 0.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 383.500000 | NaN | 0.000000 | 477.500000 | 991.500000 | NaN | NaN | NaN | NaN | 1087.000000 | 0.000000 | 0.000000 | 1464.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 3.000000 | 1.000000 | NaN | 6.000000 | NaN | 1.000000 | NaN | NaN | 1980.000000 | NaN | 2.000000 | 480.000000 | NaN | NaN | NaN | 0.000000 | 25.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | 0.000000 | 6.000000 | 2008.000000 | NaN | NaN | 163000.000000 |
| 75% | 1095.250000 | 70.000000 | NaN | 80.000000 | 11601.500000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 7.000000 | 6.000000 | 2000.000000 | 2004.000000 | NaN | NaN | NaN | NaN | NaN | 166.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 712.250000 | NaN | 0.000000 | 808.000000 | 1298.250000 | NaN | NaN | NaN | NaN | 1391.250000 | 728.000000 | 0.000000 | 1776.750000 | 1.000000 | 0.000000 | 2.000000 | 1.000000 | 3.000000 | 1.000000 | NaN | 7.000000 | NaN | 1.000000 | NaN | NaN | 2002.000000 | NaN | 2.000000 | 576.000000 | NaN | NaN | NaN | 168.000000 | 68.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | NaN | NaN | NaN | 0.000000 | 8.000000 | 2009.000000 | NaN | NaN | 214000.000000 |
| max | 1460.000000 | 190.000000 | NaN | 313.000000 | 215245.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 10.000000 | 9.000000 | 2010.000000 | 2010.000000 | NaN | NaN | NaN | NaN | NaN | 1600.000000 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 5644.000000 | NaN | 1474.000000 | 2336.000000 | 6110.000000 | NaN | NaN | NaN | NaN | 4692.000000 | 2065.000000 | 572.000000 | 5642.000000 | 3.000000 | 2.000000 | 3.000000 | 2.000000 | 8.000000 | 3.000000 | NaN | 14.000000 | NaN | 3.000000 | NaN | NaN | 2010.000000 | NaN | 4.000000 | 1418.000000 | NaN | NaN | NaN | 857.000000 | 547.000000 | 552.000000 | 508.000000 | 480.000000 | 738.000000 | NaN | NaN | NaN | 15500.000000 | 12.000000 | 2010.000000 | NaN | NaN | 755000.000000 |
df.shape
(1460, 81)
df.columns
Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',
'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',
'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',
'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',
'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',
'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',
'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',
'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',
'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',
'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',
'HalfBath', 'Bedroom', 'Kitchen', 'KitchenQual', 'TotRmsAbvGrd',
'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType', 'GarageYrBlt',
'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual', 'GarageCond',
'PavedDrive', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch',
'ScreenPorch', 'PoolArea', 'PoolQC', 'Fence', 'MiscFeature', 'MiscVal',
'MoSold', 'YrSold', 'SaleType', 'SaleCondition', 'SalePrice'],
dtype='object')
df.describe().columns
Index(['Id', 'MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual',
'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1',
'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF',
'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',
'HalfBath', 'Bedroom', 'Kitchen', 'TotRmsAbvGrd', 'Fireplaces',
'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF',
'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal',
'MoSold', 'YrSold', 'SalePrice'],
dtype='object')
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1460 entries, 0 to 1459 Data columns (total 81 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Id 1460 non-null int64 1 MSSubClass 1460 non-null int64 2 MSZoning 1460 non-null object 3 LotFrontage 1201 non-null float64 4 LotArea 1460 non-null int64 5 Street 1460 non-null object 6 Alley 91 non-null object 7 LotShape 1460 non-null object 8 LandContour 1460 non-null object 9 Utilities 1460 non-null object 10 LotConfig 1460 non-null object 11 LandSlope 1460 non-null object 12 Neighborhood 1460 non-null object 13 Condition1 1460 non-null object 14 Condition2 1460 non-null object 15 BldgType 1460 non-null object 16 HouseStyle 1460 non-null object 17 OverallQual 1460 non-null int64 18 OverallCond 1460 non-null int64 19 YearBuilt 1460 non-null int64 20 YearRemodAdd 1460 non-null int64 21 RoofStyle 1460 non-null object 22 RoofMatl 1460 non-null object 23 Exterior1st 1460 non-null object 24 Exterior2nd 1460 non-null object 25 MasVnrType 1452 non-null object 26 MasVnrArea 1452 non-null float64 27 ExterQual 1460 non-null object 28 ExterCond 1460 non-null object 29 Foundation 1460 non-null object 30 BsmtQual 1423 non-null object 31 BsmtCond 1423 non-null object 32 BsmtExposure 1422 non-null object 33 BsmtFinType1 1423 non-null object 34 BsmtFinSF1 1460 non-null int64 35 BsmtFinType2 1422 non-null object 36 BsmtFinSF2 1460 non-null int64 37 BsmtUnfSF 1460 non-null int64 38 TotalBsmtSF 1460 non-null int64 39 Heating 1460 non-null object 40 HeatingQC 1460 non-null object 41 CentralAir 1460 non-null object 42 Electrical 1459 non-null object 43 1stFlrSF 1460 non-null int64 44 2ndFlrSF 1460 non-null int64 45 LowQualFinSF 1460 non-null int64 46 GrLivArea 1460 non-null int64 47 BsmtFullBath 1460 non-null int64 48 BsmtHalfBath 1460 non-null int64 49 FullBath 1460 non-null int64 50 HalfBath 1460 non-null int64 51 Bedroom 1460 non-null int64 52 Kitchen 1460 non-null int64 53 KitchenQual 1460 non-null object 54 TotRmsAbvGrd 1460 non-null int64 55 Functional 1460 non-null object 56 Fireplaces 1460 non-null int64 57 FireplaceQu 770 non-null object 58 GarageType 1379 non-null object 59 GarageYrBlt 1379 non-null float64 60 GarageFinish 1379 non-null object 61 GarageCars 1460 non-null int64 62 GarageArea 1460 non-null int64 63 GarageQual 1379 non-null object 64 GarageCond 1379 non-null object 65 PavedDrive 1460 non-null object 66 WoodDeckSF 1460 non-null int64 67 OpenPorchSF 1460 non-null int64 68 EnclosedPorch 1460 non-null int64 69 3SsnPorch 1460 non-null int64 70 ScreenPorch 1460 non-null int64 71 PoolArea 1460 non-null int64 72 PoolQC 7 non-null object 73 Fence 281 non-null object 74 MiscFeature 54 non-null object 75 MiscVal 1460 non-null int64 76 MoSold 1460 non-null int64 77 YrSold 1460 non-null int64 78 SaleType 1460 non-null object 79 SaleCondition 1460 non-null object 80 SalePrice 1460 non-null int64 dtypes: float64(3), int64(35), object(43) memory usage: 924.0+ KB
df.size
118260
pd.set_option('display.max_rows',None)
isnull_columns = df.isna().sum()
isnull_columns = isnull_columns[isnull_columns>0]
isnull_columns
LotFrontage 259 Alley 1369 MasVnrType 8 MasVnrArea 8 BsmtQual 37 BsmtCond 37 BsmtExposure 38 BsmtFinType1 37 BsmtFinType2 38 Electrical 1 FireplaceQu 690 GarageType 81 GarageYrBlt 81 GarageFinish 81 GarageQual 81 GarageCond 81 PoolQC 1453 Fence 1179 MiscFeature 1406 dtype: int64
isnull_col_re = isnull_columns[isnull_columns>1100]
list = isnull_col_re.index[0:].tolist()
print(list)
['Alley', 'PoolQC', 'Fence', 'MiscFeature']
df = df.drop(columns=list, axis=1)
isnull_columns = df.isna().sum()
isnull_columns = isnull_columns[isnull_columns>0]
isnull_columns
LotFrontage 259 MasVnrType 8 MasVnrArea 8 BsmtQual 37 BsmtCond 37 BsmtExposure 38 BsmtFinType1 37 BsmtFinType2 38 Electrical 1 FireplaceQu 690 GarageType 81 GarageYrBlt 81 GarageFinish 81 GarageQual 81 GarageCond 81 dtype: int64
df.shape
(1460, 77)
df_n = df.select_dtypes(exclude = 'object')
df_c = df.select_dtypes(include = 'object')
df_c['Id'] = df['Id']
df_c['SalePrice'] = df['SalePrice']
df_c.head()
| MSZoning | Street | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinType2 | Heating | HeatingQC | CentralAir | Electrical | KitchenQual | Functional | FireplaceQu | GarageType | GarageFinish | GarageQual | GarageCond | PavedDrive | SaleType | SaleCondition | Id | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | RL | Pave | Reg | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | No | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | NaN | Attchd | RFn | TA | TA | Y | WD | Normal | 1 | 208500 |
| 1 | RL | Pave | Reg | Lvl | AllPub | FR2 | Gtl | Veenker | Feedr | Norm | 1Fam | 1Story | Gable | CompShg | MetalSd | MetalSd | None | TA | TA | CBlock | Gd | TA | Gd | ALQ | Unf | GasA | Ex | Y | SBrkr | TA | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 2 | 181500 |
| 2 | RL | Pave | IR1 | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | Mn | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 3 | 223500 |
| 3 | RL | Pave | IR1 | Lvl | AllPub | Corner | Gtl | Crawfor | Norm | Norm | 1Fam | 2Story | Gable | CompShg | Wd Sdng | Wd Shng | None | TA | TA | BrkTil | TA | Gd | No | ALQ | Unf | GasA | Gd | Y | SBrkr | Gd | Typ | Gd | Detchd | Unf | TA | TA | Y | WD | Abnorml | 4 | 140000 |
| 4 | RL | Pave | IR1 | Lvl | AllPub | FR2 | Gtl | NoRidge | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | Av | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 5 | 250000 |
first_column = df_c.pop('Id')
df_c.insert(0, 'Id', first_column)
df_c.head()
| Id | MSZoning | Street | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinType2 | Heating | HeatingQC | CentralAir | Electrical | KitchenQual | Functional | FireplaceQu | GarageType | GarageFinish | GarageQual | GarageCond | PavedDrive | SaleType | SaleCondition | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | RL | Pave | Reg | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | No | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | NaN | Attchd | RFn | TA | TA | Y | WD | Normal | 208500 |
| 1 | 2 | RL | Pave | Reg | Lvl | AllPub | FR2 | Gtl | Veenker | Feedr | Norm | 1Fam | 1Story | Gable | CompShg | MetalSd | MetalSd | None | TA | TA | CBlock | Gd | TA | Gd | ALQ | Unf | GasA | Ex | Y | SBrkr | TA | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 181500 |
| 2 | 3 | RL | Pave | IR1 | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | Mn | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 223500 |
| 3 | 4 | RL | Pave | IR1 | Lvl | AllPub | Corner | Gtl | Crawfor | Norm | Norm | 1Fam | 2Story | Gable | CompShg | Wd Sdng | Wd Shng | None | TA | TA | BrkTil | TA | Gd | No | ALQ | Unf | GasA | Gd | Y | SBrkr | Gd | Typ | Gd | Detchd | Unf | TA | TA | Y | WD | Abnorml | 140000 |
| 4 | 5 | RL | Pave | IR1 | Lvl | AllPub | FR2 | Gtl | NoRidge | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | Av | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 250000 |
df_n.describe()
| Id | MSSubClass | LotFrontage | LotArea | OverallQual | OverallCond | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | FullBath | HalfBath | Bedroom | Kitchen | TotRmsAbvGrd | Fireplaces | GarageYrBlt | GarageCars | GarageArea | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | MiscVal | MoSold | YrSold | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1460.000000 | 1460.000000 | 1201.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1452.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1379.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 | 1460.000000 |
| mean | 730.500000 | 56.897260 | 70.049958 | 10516.828082 | 6.099315 | 5.575342 | 1971.267808 | 1984.865753 | 103.685262 | 443.639726 | 46.549315 | 567.240411 | 1057.429452 | 1162.626712 | 346.992466 | 5.844521 | 1515.463699 | 0.425342 | 0.057534 | 1.565068 | 0.382877 | 2.866438 | 1.046575 | 6.517808 | 0.613014 | 1978.506164 | 1.767123 | 472.980137 | 94.244521 | 46.660274 | 21.954110 | 3.409589 | 15.060959 | 2.758904 | 43.489041 | 6.321918 | 2007.815753 | 180921.195890 |
| std | 421.610009 | 42.300571 | 24.284752 | 9981.264932 | 1.382997 | 1.112799 | 30.202904 | 20.645407 | 181.066207 | 456.098091 | 161.319273 | 441.866955 | 438.705324 | 386.587738 | 436.528436 | 48.623081 | 525.480383 | 0.518911 | 0.238753 | 0.550916 | 0.502885 | 0.815778 | 0.220338 | 1.625393 | 0.644666 | 24.689725 | 0.747315 | 213.804841 | 125.338794 | 66.256028 | 61.119149 | 29.317331 | 55.757415 | 40.177307 | 496.123024 | 2.703626 | 1.328095 | 79442.502883 |
| min | 1.000000 | 20.000000 | 21.000000 | 1300.000000 | 1.000000 | 1.000000 | 1872.000000 | 1950.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 334.000000 | 0.000000 | 0.000000 | 334.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 1900.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 2006.000000 | 34900.000000 |
| 25% | 365.750000 | 20.000000 | 59.000000 | 7553.500000 | 5.000000 | 5.000000 | 1954.000000 | 1967.000000 | 0.000000 | 0.000000 | 0.000000 | 223.000000 | 795.750000 | 882.000000 | 0.000000 | 0.000000 | 1129.500000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 2.000000 | 1.000000 | 5.000000 | 0.000000 | 1961.000000 | 1.000000 | 334.500000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 5.000000 | 2007.000000 | 129975.000000 |
| 50% | 730.500000 | 50.000000 | 69.000000 | 9478.500000 | 6.000000 | 5.000000 | 1973.000000 | 1994.000000 | 0.000000 | 383.500000 | 0.000000 | 477.500000 | 991.500000 | 1087.000000 | 0.000000 | 0.000000 | 1464.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 3.000000 | 1.000000 | 6.000000 | 1.000000 | 1980.000000 | 2.000000 | 480.000000 | 0.000000 | 25.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 6.000000 | 2008.000000 | 163000.000000 |
| 75% | 1095.250000 | 70.000000 | 80.000000 | 11601.500000 | 7.000000 | 6.000000 | 2000.000000 | 2004.000000 | 166.000000 | 712.250000 | 0.000000 | 808.000000 | 1298.250000 | 1391.250000 | 728.000000 | 0.000000 | 1776.750000 | 1.000000 | 0.000000 | 2.000000 | 1.000000 | 3.000000 | 1.000000 | 7.000000 | 1.000000 | 2002.000000 | 2.000000 | 576.000000 | 168.000000 | 68.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 8.000000 | 2009.000000 | 214000.000000 |
| max | 1460.000000 | 190.000000 | 313.000000 | 215245.000000 | 10.000000 | 9.000000 | 2010.000000 | 2010.000000 | 1600.000000 | 5644.000000 | 1474.000000 | 2336.000000 | 6110.000000 | 4692.000000 | 2065.000000 | 572.000000 | 5642.000000 | 3.000000 | 2.000000 | 3.000000 | 2.000000 | 8.000000 | 3.000000 | 14.000000 | 3.000000 | 2010.000000 | 4.000000 | 1418.000000 | 857.000000 | 547.000000 | 552.000000 | 508.000000 | 480.000000 | 738.000000 | 15500.000000 | 12.000000 | 2010.000000 | 755000.000000 |
df_n.corr()
| Id | MSSubClass | LotFrontage | LotArea | OverallQual | OverallCond | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | FullBath | HalfBath | Bedroom | Kitchen | TotRmsAbvGrd | Fireplaces | GarageYrBlt | GarageCars | GarageArea | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | MiscVal | MoSold | YrSold | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Id | 1.000000 | 0.011156 | -0.010601 | -0.033226 | -0.028365 | 0.012609 | -0.012713 | -0.021998 | -0.050298 | -0.005024 | -0.005968 | -0.007940 | -0.015415 | 0.010496 | 0.005590 | -0.044230 | 0.008273 | 0.002289 | -0.020155 | 0.005587 | 0.006784 | 0.037719 | 0.002951 | 0.027239 | -0.019772 | 0.000072 | 0.016570 | 0.017634 | -0.029643 | -0.000477 | 0.002889 | -0.046635 | 0.001330 | 0.057044 | -0.006242 | 0.021172 | 0.000712 | -0.021917 |
| MSSubClass | 0.011156 | 1.000000 | -0.386347 | -0.139781 | 0.032628 | -0.059316 | 0.027850 | 0.040581 | 0.022936 | -0.069836 | -0.065649 | -0.140759 | -0.238518 | -0.251758 | 0.307886 | 0.046474 | 0.074853 | 0.003491 | -0.002333 | 0.131608 | 0.177354 | -0.023438 | 0.281721 | 0.040380 | -0.045569 | 0.085072 | -0.040110 | -0.098672 | -0.012579 | -0.006100 | -0.012037 | -0.043825 | -0.026030 | 0.008283 | -0.007683 | -0.013585 | -0.021407 | -0.084284 |
| LotFrontage | -0.010601 | -0.386347 | 1.000000 | 0.426095 | 0.251646 | -0.059213 | 0.123349 | 0.088866 | 0.193458 | 0.233633 | 0.049900 | 0.132644 | 0.392075 | 0.457181 | 0.080177 | 0.038469 | 0.402797 | 0.100949 | -0.007234 | 0.198769 | 0.053532 | 0.263170 | -0.006069 | 0.352096 | 0.266639 | 0.070250 | 0.285691 | 0.344997 | 0.088521 | 0.151972 | 0.010700 | 0.070029 | 0.041383 | 0.206167 | 0.003368 | 0.011200 | 0.007450 | 0.351799 |
| LotArea | -0.033226 | -0.139781 | 0.426095 | 1.000000 | 0.105806 | -0.005636 | 0.014228 | 0.013788 | 0.104160 | 0.214103 | 0.111170 | -0.002618 | 0.260833 | 0.299475 | 0.050986 | 0.004779 | 0.263116 | 0.158155 | 0.048046 | 0.126031 | 0.014259 | 0.119690 | -0.017784 | 0.190015 | 0.271364 | -0.024947 | 0.154871 | 0.180403 | 0.171698 | 0.084774 | -0.018340 | 0.020423 | 0.043160 | 0.077672 | 0.038068 | 0.001205 | -0.014261 | 0.263843 |
| OverallQual | -0.028365 | 0.032628 | 0.251646 | 0.105806 | 1.000000 | -0.091932 | 0.572323 | 0.550684 | 0.411876 | 0.239666 | -0.059119 | 0.308159 | 0.537808 | 0.476224 | 0.295493 | -0.030429 | 0.593007 | 0.111098 | -0.040150 | 0.550600 | 0.273458 | 0.101676 | -0.183882 | 0.427452 | 0.396765 | 0.547766 | 0.600671 | 0.562022 | 0.238923 | 0.308819 | -0.113937 | 0.030371 | 0.064886 | 0.065166 | -0.031406 | 0.070815 | -0.027347 | 0.790982 |
| OverallCond | 0.012609 | -0.059316 | -0.059213 | -0.005636 | -0.091932 | 1.000000 | -0.375983 | 0.073741 | -0.128101 | -0.046231 | 0.040229 | -0.136841 | -0.171098 | -0.144203 | 0.028942 | 0.025494 | -0.079686 | -0.054942 | 0.117821 | -0.194149 | -0.060769 | 0.012980 | -0.087001 | -0.057583 | -0.023820 | -0.324297 | -0.185758 | -0.151521 | -0.003334 | -0.032589 | 0.070356 | 0.025504 | 0.054811 | -0.001985 | 0.068777 | -0.003511 | 0.043950 | -0.077856 |
| YearBuilt | -0.012713 | 0.027850 | 0.123349 | 0.014228 | 0.572323 | -0.375983 | 1.000000 | 0.592855 | 0.315707 | 0.249503 | -0.049107 | 0.149040 | 0.391452 | 0.281986 | 0.010308 | -0.183784 | 0.199010 | 0.187599 | -0.038162 | 0.468271 | 0.242656 | -0.070651 | -0.174800 | 0.095589 | 0.147716 | 0.825667 | 0.537850 | 0.478954 | 0.224880 | 0.188686 | -0.387268 | 0.031355 | -0.050364 | 0.004950 | -0.034383 | 0.012398 | -0.013618 | 0.522897 |
| YearRemodAdd | -0.021998 | 0.040581 | 0.088866 | 0.013788 | 0.550684 | 0.073741 | 0.592855 | 1.000000 | 0.179618 | 0.128451 | -0.067759 | 0.181133 | 0.291066 | 0.240379 | 0.140024 | -0.062419 | 0.287389 | 0.119470 | -0.012337 | 0.439046 | 0.183331 | -0.040581 | -0.149598 | 0.191740 | 0.112581 | 0.642277 | 0.420622 | 0.371600 | 0.205726 | 0.226298 | -0.193919 | 0.045286 | -0.038740 | 0.005829 | -0.010286 | 0.021490 | 0.035743 | 0.507101 |
| MasVnrArea | -0.050298 | 0.022936 | 0.193458 | 0.104160 | 0.411876 | -0.128101 | 0.315707 | 0.179618 | 1.000000 | 0.264736 | -0.072319 | 0.114442 | 0.363936 | 0.344501 | 0.174561 | -0.069071 | 0.390857 | 0.085310 | 0.026673 | 0.276833 | 0.201444 | 0.102821 | -0.037610 | 0.280682 | 0.249070 | 0.252691 | 0.364204 | 0.373066 | 0.159718 | 0.125703 | -0.110204 | 0.018796 | 0.061466 | 0.011723 | -0.029815 | -0.005965 | -0.008201 | 0.477493 |
| BsmtFinSF1 | -0.005024 | -0.069836 | 0.233633 | 0.214103 | 0.239666 | -0.046231 | 0.249503 | 0.128451 | 0.264736 | 1.000000 | -0.050117 | -0.495251 | 0.522396 | 0.445863 | -0.137079 | -0.064503 | 0.208171 | 0.649212 | 0.067418 | 0.058543 | 0.004262 | -0.107355 | -0.081007 | 0.044316 | 0.260011 | 0.153484 | 0.224054 | 0.296970 | 0.204306 | 0.111761 | -0.102303 | 0.026451 | 0.062021 | 0.140491 | 0.003571 | -0.015727 | 0.014359 | 0.386420 |
| BsmtFinSF2 | -0.005968 | -0.065649 | 0.049900 | 0.111170 | -0.059119 | 0.040229 | -0.049107 | -0.067759 | -0.072319 | -0.050117 | 1.000000 | -0.209294 | 0.104810 | 0.097117 | -0.099260 | 0.014807 | -0.009640 | 0.158678 | 0.070948 | -0.076444 | -0.032148 | -0.015728 | -0.040751 | -0.035227 | 0.046921 | -0.088011 | -0.038264 | -0.018227 | 0.067898 | 0.003093 | 0.036543 | -0.029993 | 0.088871 | 0.041709 | 0.004940 | -0.015211 | 0.031706 | -0.011378 |
| BsmtUnfSF | -0.007940 | -0.140759 | 0.132644 | -0.002618 | 0.308159 | -0.136841 | 0.149040 | 0.181133 | 0.114442 | -0.495251 | -0.209294 | 1.000000 | 0.415360 | 0.317987 | 0.004469 | 0.028167 | 0.240257 | -0.422900 | -0.095804 | 0.288886 | -0.041118 | 0.166643 | 0.030086 | 0.250647 | 0.051575 | 0.190708 | 0.214175 | 0.183303 | -0.005316 | 0.129005 | -0.002538 | 0.020764 | -0.012579 | -0.035092 | -0.023837 | 0.034888 | -0.041258 | 0.214479 |
| TotalBsmtSF | -0.015415 | -0.238518 | 0.392075 | 0.260833 | 0.537808 | -0.171098 | 0.391452 | 0.291066 | 0.363936 | 0.522396 | 0.104810 | 0.415360 | 1.000000 | 0.819530 | -0.174512 | -0.033245 | 0.454868 | 0.307351 | -0.000315 | 0.323722 | -0.048804 | 0.050450 | -0.068901 | 0.285573 | 0.339519 | 0.322445 | 0.434585 | 0.486665 | 0.232019 | 0.247264 | -0.095478 | 0.037384 | 0.084489 | 0.126053 | -0.018479 | 0.013196 | -0.014969 | 0.613581 |
| 1stFlrSF | 0.010496 | -0.251758 | 0.457181 | 0.299475 | 0.476224 | -0.144203 | 0.281986 | 0.240379 | 0.344501 | 0.445863 | 0.097117 | 0.317987 | 0.819530 | 1.000000 | -0.202646 | -0.014241 | 0.566024 | 0.244671 | 0.001956 | 0.380637 | -0.119916 | 0.127401 | 0.068101 | 0.409516 | 0.410531 | 0.233449 | 0.439317 | 0.489782 | 0.235459 | 0.211671 | -0.065292 | 0.056104 | 0.088758 | 0.131525 | -0.021096 | 0.031372 | -0.013604 | 0.605852 |
| 2ndFlrSF | 0.005590 | 0.307886 | 0.080177 | 0.050986 | 0.295493 | 0.028942 | 0.010308 | 0.140024 | 0.174561 | -0.137079 | -0.099260 | 0.004469 | -0.174512 | -0.202646 | 1.000000 | 0.063353 | 0.687501 | -0.169494 | -0.023855 | 0.421378 | 0.609707 | 0.502901 | 0.059306 | 0.616423 | 0.194561 | 0.070832 | 0.183926 | 0.138347 | 0.092165 | 0.208026 | 0.061989 | -0.024358 | 0.040606 | 0.081487 | 0.016197 | 0.035164 | -0.028700 | 0.319334 |
| LowQualFinSF | -0.044230 | 0.046474 | 0.038469 | 0.004779 | -0.030429 | 0.025494 | -0.183784 | -0.062419 | -0.069071 | -0.064503 | 0.014807 | 0.028167 | -0.033245 | -0.014241 | 0.063353 | 1.000000 | 0.134683 | -0.047143 | -0.005842 | -0.000710 | -0.027080 | 0.105607 | 0.007522 | 0.131185 | -0.021272 | -0.036363 | -0.094480 | -0.067601 | -0.025444 | 0.018251 | 0.061081 | -0.004296 | 0.026799 | 0.062157 | -0.003793 | -0.022174 | -0.028921 | -0.025606 |
| GrLivArea | 0.008273 | 0.074853 | 0.402797 | 0.263116 | 0.593007 | -0.079686 | 0.199010 | 0.287389 | 0.390857 | 0.208171 | -0.009640 | 0.240257 | 0.454868 | 0.566024 | 0.687501 | 0.134683 | 1.000000 | 0.034836 | -0.018918 | 0.630012 | 0.415772 | 0.521270 | 0.100063 | 0.825489 | 0.461679 | 0.231197 | 0.467247 | 0.468997 | 0.247433 | 0.330224 | 0.009113 | 0.020643 | 0.101510 | 0.170205 | -0.002416 | 0.050240 | -0.036526 | 0.708624 |
| BsmtFullBath | 0.002289 | 0.003491 | 0.100949 | 0.158155 | 0.111098 | -0.054942 | 0.187599 | 0.119470 | 0.085310 | 0.649212 | 0.158678 | -0.422900 | 0.307351 | 0.244671 | -0.169494 | -0.047143 | 0.034836 | 1.000000 | -0.147871 | -0.064512 | -0.030905 | -0.150673 | -0.041503 | -0.053275 | 0.137928 | 0.124553 | 0.131881 | 0.179189 | 0.175315 | 0.067341 | -0.049911 | -0.000106 | 0.023148 | 0.067616 | -0.023047 | -0.025361 | 0.067049 | 0.227122 |
| BsmtHalfBath | -0.020155 | -0.002333 | -0.007234 | 0.048046 | -0.040150 | 0.117821 | -0.038162 | -0.012337 | 0.026673 | 0.067418 | 0.070948 | -0.095804 | -0.000315 | 0.001956 | -0.023855 | -0.005842 | -0.018918 | -0.147871 | 1.000000 | -0.054536 | -0.012340 | 0.046519 | -0.037944 | -0.023836 | 0.028976 | -0.077464 | -0.020891 | -0.024536 | 0.040161 | -0.025324 | -0.008555 | 0.035114 | 0.032121 | 0.020025 | -0.007367 | 0.032873 | -0.046524 | -0.016844 |
| FullBath | 0.005587 | 0.131608 | 0.198769 | 0.126031 | 0.550600 | -0.194149 | 0.468271 | 0.439046 | 0.276833 | 0.058543 | -0.076444 | 0.288886 | 0.323722 | 0.380637 | 0.421378 | -0.000710 | 0.630012 | -0.064512 | -0.054536 | 1.000000 | 0.136381 | 0.363252 | 0.133115 | 0.554784 | 0.243671 | 0.484557 | 0.469672 | 0.405656 | 0.187703 | 0.259977 | -0.115093 | 0.035353 | -0.008106 | 0.049604 | -0.014290 | 0.055872 | -0.019669 | 0.560664 |
| HalfBath | 0.006784 | 0.177354 | 0.053532 | 0.014259 | 0.273458 | -0.060769 | 0.242656 | 0.183331 | 0.201444 | 0.004262 | -0.032148 | -0.041118 | -0.048804 | -0.119916 | 0.609707 | -0.027080 | 0.415772 | -0.030905 | -0.012340 | 0.136381 | 1.000000 | 0.226651 | -0.068263 | 0.343415 | 0.203649 | 0.196785 | 0.219178 | 0.163549 | 0.108080 | 0.199740 | -0.095317 | -0.004972 | 0.072426 | 0.022381 | 0.001290 | -0.009050 | -0.010269 | 0.284108 |
| Bedroom | 0.037719 | -0.023438 | 0.263170 | 0.119690 | 0.101676 | 0.012980 | -0.070651 | -0.040581 | 0.102821 | -0.107355 | -0.015728 | 0.166643 | 0.050450 | 0.127401 | 0.502901 | 0.105607 | 0.521270 | -0.150673 | 0.046519 | 0.363252 | 0.226651 | 1.000000 | 0.198597 | 0.676620 | 0.107570 | -0.064518 | 0.086106 | 0.065253 | 0.046854 | 0.093810 | 0.041570 | -0.024478 | 0.044300 | 0.070703 | 0.007767 | 0.046544 | -0.036014 | 0.168213 |
| Kitchen | 0.002951 | 0.281721 | -0.006069 | -0.017784 | -0.183882 | -0.087001 | -0.174800 | -0.149598 | -0.037610 | -0.081007 | -0.040751 | 0.030086 | -0.068901 | 0.068101 | 0.059306 | 0.007522 | 0.100063 | -0.041503 | -0.037944 | 0.133115 | -0.068263 | 0.198597 | 1.000000 | 0.256045 | -0.123936 | -0.124411 | -0.050634 | -0.064433 | -0.090130 | -0.070091 | 0.037312 | -0.024600 | -0.051613 | -0.014525 | 0.062341 | 0.026589 | 0.031687 | -0.135907 |
| TotRmsAbvGrd | 0.027239 | 0.040380 | 0.352096 | 0.190015 | 0.427452 | -0.057583 | 0.095589 | 0.191740 | 0.280682 | 0.044316 | -0.035227 | 0.250647 | 0.285573 | 0.409516 | 0.616423 | 0.131185 | 0.825489 | -0.053275 | -0.023836 | 0.554784 | 0.343415 | 0.676620 | 0.256045 | 1.000000 | 0.326114 | 0.148112 | 0.362289 | 0.337822 | 0.165984 | 0.234192 | 0.004151 | -0.006683 | 0.059383 | 0.083757 | 0.024763 | 0.036907 | -0.034516 | 0.533723 |
| Fireplaces | -0.019772 | -0.045569 | 0.266639 | 0.271364 | 0.396765 | -0.023820 | 0.147716 | 0.112581 | 0.249070 | 0.260011 | 0.046921 | 0.051575 | 0.339519 | 0.410531 | 0.194561 | -0.021272 | 0.461679 | 0.137928 | 0.028976 | 0.243671 | 0.203649 | 0.107570 | -0.123936 | 0.326114 | 1.000000 | 0.046822 | 0.300789 | 0.269141 | 0.200019 | 0.169405 | -0.024822 | 0.011257 | 0.184530 | 0.095074 | 0.001409 | 0.046357 | -0.024096 | 0.466929 |
| GarageYrBlt | 0.000072 | 0.085072 | 0.070250 | -0.024947 | 0.547766 | -0.324297 | 0.825667 | 0.642277 | 0.252691 | 0.153484 | -0.088011 | 0.190708 | 0.322445 | 0.233449 | 0.070832 | -0.036363 | 0.231197 | 0.124553 | -0.077464 | 0.484557 | 0.196785 | -0.064518 | -0.124411 | 0.148112 | 0.046822 | 1.000000 | 0.588920 | 0.564567 | 0.224577 | 0.228425 | -0.297003 | 0.023544 | -0.075418 | -0.014501 | -0.032417 | 0.005337 | -0.001014 | 0.486362 |
| GarageCars | 0.016570 | -0.040110 | 0.285691 | 0.154871 | 0.600671 | -0.185758 | 0.537850 | 0.420622 | 0.364204 | 0.224054 | -0.038264 | 0.214175 | 0.434585 | 0.439317 | 0.183926 | -0.094480 | 0.467247 | 0.131881 | -0.020891 | 0.469672 | 0.219178 | 0.086106 | -0.050634 | 0.362289 | 0.300789 | 0.588920 | 1.000000 | 0.882475 | 0.226342 | 0.213569 | -0.151434 | 0.035765 | 0.050494 | 0.020934 | -0.043080 | 0.040522 | -0.039117 | 0.640409 |
| GarageArea | 0.017634 | -0.098672 | 0.344997 | 0.180403 | 0.562022 | -0.151521 | 0.478954 | 0.371600 | 0.373066 | 0.296970 | -0.018227 | 0.183303 | 0.486665 | 0.489782 | 0.138347 | -0.067601 | 0.468997 | 0.179189 | -0.024536 | 0.405656 | 0.163549 | 0.065253 | -0.064433 | 0.337822 | 0.269141 | 0.564567 | 0.882475 | 1.000000 | 0.224666 | 0.241435 | -0.121777 | 0.035087 | 0.051412 | 0.061047 | -0.027400 | 0.027974 | -0.027378 | 0.623431 |
| WoodDeckSF | -0.029643 | -0.012579 | 0.088521 | 0.171698 | 0.238923 | -0.003334 | 0.224880 | 0.205726 | 0.159718 | 0.204306 | 0.067898 | -0.005316 | 0.232019 | 0.235459 | 0.092165 | -0.025444 | 0.247433 | 0.175315 | 0.040161 | 0.187703 | 0.108080 | 0.046854 | -0.090130 | 0.165984 | 0.200019 | 0.224577 | 0.226342 | 0.224666 | 1.000000 | 0.058661 | -0.125989 | -0.032771 | -0.074181 | 0.073378 | -0.009551 | 0.021011 | 0.022270 | 0.324413 |
| OpenPorchSF | -0.000477 | -0.006100 | 0.151972 | 0.084774 | 0.308819 | -0.032589 | 0.188686 | 0.226298 | 0.125703 | 0.111761 | 0.003093 | 0.129005 | 0.247264 | 0.211671 | 0.208026 | 0.018251 | 0.330224 | 0.067341 | -0.025324 | 0.259977 | 0.199740 | 0.093810 | -0.070091 | 0.234192 | 0.169405 | 0.228425 | 0.213569 | 0.241435 | 0.058661 | 1.000000 | -0.093079 | -0.005842 | 0.074304 | 0.060762 | -0.018584 | 0.071255 | -0.057619 | 0.315856 |
| EnclosedPorch | 0.002889 | -0.012037 | 0.010700 | -0.018340 | -0.113937 | 0.070356 | -0.387268 | -0.193919 | -0.110204 | -0.102303 | 0.036543 | -0.002538 | -0.095478 | -0.065292 | 0.061989 | 0.061081 | 0.009113 | -0.049911 | -0.008555 | -0.115093 | -0.095317 | 0.041570 | 0.037312 | 0.004151 | -0.024822 | -0.297003 | -0.151434 | -0.121777 | -0.125989 | -0.093079 | 1.000000 | -0.037305 | -0.082864 | 0.054203 | 0.018361 | -0.028887 | -0.009916 | -0.128578 |
| 3SsnPorch | -0.046635 | -0.043825 | 0.070029 | 0.020423 | 0.030371 | 0.025504 | 0.031355 | 0.045286 | 0.018796 | 0.026451 | -0.029993 | 0.020764 | 0.037384 | 0.056104 | -0.024358 | -0.004296 | 0.020643 | -0.000106 | 0.035114 | 0.035353 | -0.004972 | -0.024478 | -0.024600 | -0.006683 | 0.011257 | 0.023544 | 0.035765 | 0.035087 | -0.032771 | -0.005842 | -0.037305 | 1.000000 | -0.031436 | -0.007992 | 0.000354 | 0.029474 | 0.018645 | 0.044584 |
| ScreenPorch | 0.001330 | -0.026030 | 0.041383 | 0.043160 | 0.064886 | 0.054811 | -0.050364 | -0.038740 | 0.061466 | 0.062021 | 0.088871 | -0.012579 | 0.084489 | 0.088758 | 0.040606 | 0.026799 | 0.101510 | 0.023148 | 0.032121 | -0.008106 | 0.072426 | 0.044300 | -0.051613 | 0.059383 | 0.184530 | -0.075418 | 0.050494 | 0.051412 | -0.074181 | 0.074304 | -0.082864 | -0.031436 | 1.000000 | 0.051307 | 0.031946 | 0.023217 | 0.010694 | 0.111447 |
| PoolArea | 0.057044 | 0.008283 | 0.206167 | 0.077672 | 0.065166 | -0.001985 | 0.004950 | 0.005829 | 0.011723 | 0.140491 | 0.041709 | -0.035092 | 0.126053 | 0.131525 | 0.081487 | 0.062157 | 0.170205 | 0.067616 | 0.020025 | 0.049604 | 0.022381 | 0.070703 | -0.014525 | 0.083757 | 0.095074 | -0.014501 | 0.020934 | 0.061047 | 0.073378 | 0.060762 | 0.054203 | -0.007992 | 0.051307 | 1.000000 | 0.029669 | -0.033737 | -0.059689 | 0.092404 |
| MiscVal | -0.006242 | -0.007683 | 0.003368 | 0.038068 | -0.031406 | 0.068777 | -0.034383 | -0.010286 | -0.029815 | 0.003571 | 0.004940 | -0.023837 | -0.018479 | -0.021096 | 0.016197 | -0.003793 | -0.002416 | -0.023047 | -0.007367 | -0.014290 | 0.001290 | 0.007767 | 0.062341 | 0.024763 | 0.001409 | -0.032417 | -0.043080 | -0.027400 | -0.009551 | -0.018584 | 0.018361 | 0.000354 | 0.031946 | 0.029669 | 1.000000 | -0.006495 | 0.004906 | -0.021190 |
| MoSold | 0.021172 | -0.013585 | 0.011200 | 0.001205 | 0.070815 | -0.003511 | 0.012398 | 0.021490 | -0.005965 | -0.015727 | -0.015211 | 0.034888 | 0.013196 | 0.031372 | 0.035164 | -0.022174 | 0.050240 | -0.025361 | 0.032873 | 0.055872 | -0.009050 | 0.046544 | 0.026589 | 0.036907 | 0.046357 | 0.005337 | 0.040522 | 0.027974 | 0.021011 | 0.071255 | -0.028887 | 0.029474 | 0.023217 | -0.033737 | -0.006495 | 1.000000 | -0.145721 | 0.046432 |
| YrSold | 0.000712 | -0.021407 | 0.007450 | -0.014261 | -0.027347 | 0.043950 | -0.013618 | 0.035743 | -0.008201 | 0.014359 | 0.031706 | -0.041258 | -0.014969 | -0.013604 | -0.028700 | -0.028921 | -0.036526 | 0.067049 | -0.046524 | -0.019669 | -0.010269 | -0.036014 | 0.031687 | -0.034516 | -0.024096 | -0.001014 | -0.039117 | -0.027378 | 0.022270 | -0.057619 | -0.009916 | 0.018645 | 0.010694 | -0.059689 | 0.004906 | -0.145721 | 1.000000 | -0.028923 |
| SalePrice | -0.021917 | -0.084284 | 0.351799 | 0.263843 | 0.790982 | -0.077856 | 0.522897 | 0.507101 | 0.477493 | 0.386420 | -0.011378 | 0.214479 | 0.613581 | 0.605852 | 0.319334 | -0.025606 | 0.708624 | 0.227122 | -0.016844 | 0.560664 | 0.284108 | 0.168213 | -0.135907 | 0.533723 | 0.466929 | 0.486362 | 0.640409 | 0.623431 | 0.324413 | 0.315856 | -0.128578 | 0.044584 | 0.111447 | 0.092404 | -0.021190 | 0.046432 | -0.028923 | 1.000000 |
sns.set(rc={'figure.figsize':(30,30)})
sns.heatmap(data=df_n.corr(), square=True, annot=True, fmt='.2g', cmap='summer')
<AxesSubplot: >
df_n_high = df_n.loc[:, df_n.corr().abs()['SalePrice'] > 0.3]
df_n_high['Id'] = df_n['Id']
first_column = df_n_high.pop('Id')
df_n_high.insert(0, 'Id', first_column)
df_n_high.head()
| Id | LotFrontage | OverallQual | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | TotalBsmtSF | 1stFlrSF | 2ndFlrSF | GrLivArea | FullBath | TotRmsAbvGrd | Fireplaces | GarageYrBlt | GarageCars | GarageArea | WoodDeckSF | OpenPorchSF | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 65.0 | 7 | 2003 | 2003 | 196.0 | 706 | 856 | 856 | 854 | 1710 | 2 | 8 | 0 | 2003.0 | 2 | 548 | 0 | 61 | 208500 |
| 1 | 2 | 80.0 | 6 | 1976 | 1976 | 0.0 | 978 | 1262 | 1262 | 0 | 1262 | 2 | 6 | 1 | 1976.0 | 2 | 460 | 298 | 0 | 181500 |
| 2 | 3 | 68.0 | 7 | 2001 | 2002 | 162.0 | 486 | 920 | 920 | 866 | 1786 | 2 | 6 | 1 | 2001.0 | 2 | 608 | 0 | 42 | 223500 |
| 3 | 4 | 60.0 | 7 | 1915 | 1970 | 0.0 | 216 | 756 | 961 | 756 | 1717 | 1 | 7 | 1 | 1998.0 | 3 | 642 | 0 | 35 | 140000 |
| 4 | 5 | 84.0 | 8 | 2000 | 2000 | 350.0 | 655 | 1145 | 1145 | 1053 | 2198 | 2 | 9 | 1 | 2000.0 | 3 | 836 | 192 | 84 | 250000 |
sns.set(rc={'figure.figsize':(30,30)})
sns.heatmap(data=df_n_high.corr(), square=True, annot=True, fmt='.2g', cmap="summer")
<AxesSubplot: >
df_n = df_n_high
null_columns = df_n.isna().sum()
null_columns = null_columns[null_columns > 0]
null_columns
LotFrontage 259 MasVnrArea 8 GarageYrBlt 81 dtype: int64
list = null_columns.index[0:].tolist()
print(list)
['LotFrontage', 'MasVnrArea', 'GarageYrBlt']
def replaceNan(list, df):
for i in list:
print('UPDATING: ' + str(i))
df[i].fillna(value=df[i].mean().astype('float'), inplace=True)
return df
df_n = replaceNan(list,df_n)
df_n.head()
UPDATING: LotFrontage UPDATING: MasVnrArea UPDATING: GarageYrBlt
| Id | LotFrontage | OverallQual | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | TotalBsmtSF | 1stFlrSF | 2ndFlrSF | GrLivArea | FullBath | TotRmsAbvGrd | Fireplaces | GarageYrBlt | GarageCars | GarageArea | WoodDeckSF | OpenPorchSF | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 65.0 | 7 | 2003 | 2003 | 196.0 | 706 | 856 | 856 | 854 | 1710 | 2 | 8 | 0 | 2003.0 | 2 | 548 | 0 | 61 | 208500 |
| 1 | 2 | 80.0 | 6 | 1976 | 1976 | 0.0 | 978 | 1262 | 1262 | 0 | 1262 | 2 | 6 | 1 | 1976.0 | 2 | 460 | 298 | 0 | 181500 |
| 2 | 3 | 68.0 | 7 | 2001 | 2002 | 162.0 | 486 | 920 | 920 | 866 | 1786 | 2 | 6 | 1 | 2001.0 | 2 | 608 | 0 | 42 | 223500 |
| 3 | 4 | 60.0 | 7 | 1915 | 1970 | 0.0 | 216 | 756 | 961 | 756 | 1717 | 1 | 7 | 1 | 1998.0 | 3 | 642 | 0 | 35 | 140000 |
| 4 | 5 | 84.0 | 8 | 2000 | 2000 | 350.0 | 655 | 1145 | 1145 | 1053 | 2198 | 2 | 9 | 1 | 2000.0 | 3 | 836 | 192 | 84 | 250000 |
df_n.head()
| Id | LotFrontage | OverallQual | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | TotalBsmtSF | 1stFlrSF | 2ndFlrSF | GrLivArea | FullBath | TotRmsAbvGrd | Fireplaces | GarageYrBlt | GarageCars | GarageArea | WoodDeckSF | OpenPorchSF | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 65.0 | 7 | 2003 | 2003 | 196.0 | 706 | 856 | 856 | 854 | 1710 | 2 | 8 | 0 | 2003.0 | 2 | 548 | 0 | 61 | 208500 |
| 1 | 2 | 80.0 | 6 | 1976 | 1976 | 0.0 | 978 | 1262 | 1262 | 0 | 1262 | 2 | 6 | 1 | 1976.0 | 2 | 460 | 298 | 0 | 181500 |
| 2 | 3 | 68.0 | 7 | 2001 | 2002 | 162.0 | 486 | 920 | 920 | 866 | 1786 | 2 | 6 | 1 | 2001.0 | 2 | 608 | 0 | 42 | 223500 |
| 3 | 4 | 60.0 | 7 | 1915 | 1970 | 0.0 | 216 | 756 | 961 | 756 | 1717 | 1 | 7 | 1 | 1998.0 | 3 | 642 | 0 | 35 | 140000 |
| 4 | 5 | 84.0 | 8 | 2000 | 2000 | 350.0 | 655 | 1145 | 1145 | 1053 | 2198 | 2 | 9 | 1 | 2000.0 | 3 | 836 | 192 | 84 | 250000 |
null_columns = df_n.isna().sum()
null_columns = null_columns[null_columns > 0]
null_columns
Series([], dtype: int64)
def boxplotloop(df, columns):
for col in columns:
if df[col].dtype != object:
sns.set(rc={'figure.figsize':(11.7,8.27)})
sns.boxplot(df[col])
plt.show()
boxplotloop(df_n, df_n.describe().columns)
def cleanup_outliers(df,columns):
for col in columns:
print('Working on column: {}'.format(col))
if (df[col].dtype != object) :
q1,q3 = np.percentile(df[col], [25,75])
iqr = q3-q1
minv = q1-(1.5*iqr)
maxv = q3+(1.5*iqr)
med = df[col].median()
df[col] = np.where(df[col]>maxv , maxv, df[col]).astype(df[col].dtype)
df[col] = np.where(df[col]<minv , minv, df[col]).astype(df[col].dtype)
return df
df2_n = cleanup_outliers(df_n, df_n.columns)
Working on column: Id Working on column: LotFrontage Working on column: OverallQual Working on column: YearBuilt Working on column: YearRemodAdd Working on column: MasVnrArea Working on column: BsmtFinSF1 Working on column: TotalBsmtSF Working on column: 1stFlrSF Working on column: 2ndFlrSF Working on column: GrLivArea Working on column: FullBath Working on column: TotRmsAbvGrd Working on column: Fireplaces Working on column: GarageYrBlt Working on column: GarageCars Working on column: GarageArea Working on column: WoodDeckSF Working on column: OpenPorchSF Working on column: SalePrice
boxplotloop(df2_n, df2_n.describe().columns)
df2_n.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1460 entries, 0 to 1459 Data columns (total 20 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Id 1460 non-null int64 1 LotFrontage 1460 non-null float64 2 OverallQual 1460 non-null int64 3 YearBuilt 1460 non-null int64 4 YearRemodAdd 1460 non-null int64 5 MasVnrArea 1460 non-null float64 6 BsmtFinSF1 1460 non-null int64 7 TotalBsmtSF 1460 non-null int64 8 1stFlrSF 1460 non-null int64 9 2ndFlrSF 1460 non-null int64 10 GrLivArea 1460 non-null int64 11 FullBath 1460 non-null int64 12 TotRmsAbvGrd 1460 non-null int64 13 Fireplaces 1460 non-null int64 14 GarageYrBlt 1460 non-null float64 15 GarageCars 1460 non-null int64 16 GarageArea 1460 non-null int64 17 WoodDeckSF 1460 non-null int64 18 OpenPorchSF 1460 non-null int64 19 SalePrice 1460 non-null int64 dtypes: float64(3), int64(17) memory usage: 228.2 KB
sns.set(rc={'figure.figsize':(30,30)})
color = plt.get_cmap('summer')
color.set_bad('lightblue')
sns.heatmap(data=df2_n.corr(), square=True, annot=True, fmt='.2g', cmap= color)
<AxesSubplot: >
df2_n.head(50)
| Id | LotFrontage | OverallQual | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | TotalBsmtSF | 1stFlrSF | 2ndFlrSF | GrLivArea | FullBath | TotRmsAbvGrd | Fireplaces | GarageYrBlt | GarageCars | GarageArea | WoodDeckSF | OpenPorchSF | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 65.000000 | 7 | 2003 | 2003 | 196.000 | 706 | 856 | 856 | 854 | 1710 | 2 | 8 | 0 | 2003.000000 | 2 | 548 | 0 | 61 | 208500 |
| 1 | 2 | 80.000000 | 6 | 1976 | 1976 | 0.000 | 978 | 1262 | 1262 | 0 | 1262 | 2 | 6 | 1 | 1976.000000 | 2 | 460 | 298 | 0 | 181500 |
| 2 | 3 | 68.000000 | 7 | 2001 | 2002 | 162.000 | 486 | 920 | 920 | 866 | 1786 | 2 | 6 | 1 | 2001.000000 | 2 | 608 | 0 | 42 | 223500 |
| 3 | 4 | 60.000000 | 7 | 1915 | 1970 | 0.000 | 216 | 756 | 961 | 756 | 1717 | 1 | 7 | 1 | 1998.000000 | 3 | 642 | 0 | 35 | 140000 |
| 4 | 5 | 84.000000 | 8 | 2000 | 2000 | 350.000 | 655 | 1145 | 1145 | 1053 | 2198 | 2 | 9 | 1 | 2000.000000 | 3 | 836 | 192 | 84 | 250000 |
| 5 | 6 | 85.000000 | 5 | 1993 | 1995 | 0.000 | 732 | 796 | 796 | 566 | 1362 | 1 | 5 | 0 | 1993.000000 | 2 | 480 | 40 | 30 | 143000 |
| 6 | 7 | 75.000000 | 8 | 2004 | 2005 | 186.000 | 1369 | 1686 | 1694 | 0 | 1694 | 2 | 7 | 1 | 2004.000000 | 2 | 636 | 255 | 57 | 307000 |
| 7 | 8 | 70.049958 | 7 | 1973 | 1973 | 240.000 | 859 | 1107 | 1107 | 983 | 2090 | 2 | 7 | 2 | 1973.000000 | 2 | 484 | 235 | 170 | 200000 |
| 8 | 9 | 51.000000 | 7 | 1931 | 1950 | 0.000 | 0 | 952 | 1022 | 752 | 1774 | 2 | 8 | 2 | 1931.000000 | 2 | 468 | 90 | 0 | 129900 |
| 9 | 10 | 50.000000 | 5 | 1939 | 1950 | 0.000 | 851 | 991 | 1077 | 0 | 1077 | 1 | 5 | 2 | 1939.000000 | 1 | 205 | 0 | 4 | 118000 |
| 10 | 11 | 70.000000 | 5 | 1965 | 1965 | 0.000 | 906 | 1040 | 1040 | 0 | 1040 | 1 | 5 | 0 | 1965.000000 | 1 | 384 | 0 | 0 | 129500 |
| 11 | 12 | 85.000000 | 9 | 2005 | 2006 | 286.000 | 998 | 1175 | 1182 | 1142 | 2324 | 3 | 10 | 2 | 2005.000000 | 3 | 736 | 147 | 21 | 340037 |
| 12 | 13 | 70.049958 | 5 | 1962 | 1962 | 0.000 | 737 | 912 | 912 | 0 | 912 | 1 | 4 | 0 | 1962.000000 | 1 | 352 | 140 | 0 | 144000 |
| 13 | 14 | 91.000000 | 7 | 2006 | 2007 | 306.000 | 0 | 1494 | 1494 | 0 | 1494 | 2 | 7 | 1 | 2006.000000 | 3 | 840 | 160 | 33 | 279500 |
| 14 | 15 | 70.049958 | 6 | 1960 | 1960 | 212.000 | 733 | 1253 | 1253 | 0 | 1253 | 1 | 5 | 1 | 1960.000000 | 1 | 352 | 0 | 170 | 157000 |
| 15 | 16 | 51.000000 | 7 | 1929 | 2001 | 0.000 | 0 | 832 | 854 | 0 | 854 | 1 | 5 | 0 | 1991.000000 | 2 | 576 | 48 | 112 | 132000 |
| 16 | 17 | 70.049958 | 6 | 1970 | 1970 | 180.000 | 578 | 1004 | 1004 | 0 | 1004 | 1 | 5 | 1 | 1970.000000 | 2 | 480 | 0 | 0 | 149000 |
| 17 | 18 | 72.000000 | 4 | 1967 | 1967 | 0.000 | 0 | 42 | 1296 | 0 | 1296 | 2 | 6 | 0 | 1967.000000 | 2 | 516 | 0 | 0 | 90000 |
| 18 | 19 | 66.000000 | 5 | 2004 | 2004 | 0.000 | 646 | 1114 | 1114 | 0 | 1114 | 1 | 6 | 0 | 2004.000000 | 2 | 576 | 0 | 102 | 159000 |
| 19 | 20 | 70.000000 | 5 | 1958 | 1965 | 0.000 | 504 | 1029 | 1339 | 0 | 1339 | 1 | 6 | 0 | 1958.000000 | 1 | 294 | 0 | 0 | 139000 |
| 20 | 21 | 101.000000 | 8 | 2005 | 2006 | 380.000 | 0 | 1158 | 1158 | 1218 | 2376 | 3 | 9 | 1 | 2005.000000 | 3 | 853 | 240 | 154 | 325300 |
| 21 | 22 | 57.000000 | 7 | 1930 | 1950 | 0.000 | 0 | 637 | 1108 | 0 | 1108 | 1 | 6 | 1 | 1930.000000 | 1 | 280 | 0 | 0 | 139400 |
| 22 | 23 | 75.000000 | 8 | 2002 | 2002 | 281.000 | 0 | 1777 | 1795 | 0 | 1795 | 2 | 7 | 1 | 2002.000000 | 2 | 534 | 171 | 159 | 230000 |
| 23 | 24 | 44.000000 | 5 | 1976 | 1976 | 0.000 | 840 | 1040 | 1060 | 0 | 1060 | 1 | 6 | 1 | 1976.000000 | 2 | 572 | 100 | 110 | 129900 |
| 24 | 25 | 70.049958 | 5 | 1968 | 2001 | 0.000 | 188 | 1060 | 1060 | 0 | 1060 | 1 | 6 | 1 | 1968.000000 | 1 | 270 | 406 | 90 | 154000 |
| 25 | 26 | 107.500000 | 8 | 2007 | 2007 | 410.625 | 0 | 1566 | 1600 | 0 | 1600 | 2 | 7 | 1 | 2007.000000 | 3 | 890 | 0 | 56 | 256300 |
| 26 | 27 | 60.000000 | 5 | 1951 | 2000 | 0.000 | 234 | 900 | 900 | 0 | 900 | 1 | 5 | 0 | 2005.000000 | 2 | 576 | 222 | 32 | 134800 |
| 27 | 28 | 98.000000 | 8 | 2007 | 2008 | 200.000 | 1218 | 1704 | 1704 | 0 | 1704 | 2 | 7 | 1 | 2008.000000 | 3 | 772 | 0 | 50 | 306000 |
| 28 | 29 | 47.000000 | 5 | 1957 | 1997 | 0.000 | 1277 | 1484 | 1600 | 0 | 1600 | 1 | 6 | 2 | 1957.000000 | 1 | 319 | 288 | 170 | 207500 |
| 29 | 30 | 60.000000 | 4 | 1927 | 1950 | 0.000 | 0 | 520 | 520 | 0 | 520 | 1 | 4 | 0 | 1920.000000 | 1 | 240 | 49 | 0 | 68500 |
| 30 | 31 | 50.000000 | 4 | 1920 | 1950 | 0.000 | 0 | 649 | 649 | 668 | 1317 | 1 | 6 | 0 | 1920.000000 | 1 | 250 | 0 | 54 | 40000 |
| 31 | 32 | 70.049958 | 5 | 1966 | 2006 | 0.000 | 0 | 1228 | 1228 | 0 | 1228 | 1 | 6 | 0 | 1966.000000 | 1 | 271 | 0 | 65 | 149350 |
| 32 | 33 | 85.000000 | 8 | 2007 | 2007 | 0.000 | 0 | 1234 | 1234 | 0 | 1234 | 2 | 7 | 0 | 2007.000000 | 2 | 484 | 0 | 30 | 179900 |
| 33 | 34 | 70.000000 | 5 | 1959 | 1959 | 0.000 | 1018 | 1398 | 1700 | 0 | 1700 | 1 | 6 | 1 | 1959.000000 | 2 | 447 | 0 | 38 | 165500 |
| 34 | 35 | 60.000000 | 9 | 2005 | 2005 | 246.000 | 1153 | 1561 | 1561 | 0 | 1561 | 2 | 6 | 1 | 2005.000000 | 2 | 556 | 203 | 47 | 277500 |
| 35 | 36 | 107.500000 | 8 | 2004 | 2005 | 132.000 | 0 | 1117 | 1132 | 1320 | 2452 | 3 | 9 | 1 | 2004.000000 | 3 | 691 | 113 | 32 | 309000 |
| 36 | 37 | 107.500000 | 5 | 1994 | 1995 | 0.000 | 0 | 1097 | 1097 | 0 | 1097 | 1 | 6 | 0 | 1995.000000 | 2 | 672 | 392 | 64 | 145000 |
| 37 | 38 | 74.000000 | 5 | 1954 | 1990 | 410.625 | 1213 | 1297 | 1297 | 0 | 1297 | 1 | 5 | 1 | 1954.000000 | 2 | 498 | 0 | 0 | 153000 |
| 38 | 39 | 68.000000 | 5 | 1953 | 2007 | 0.000 | 731 | 1057 | 1057 | 0 | 1057 | 1 | 5 | 0 | 1953.000000 | 1 | 246 | 0 | 52 | 109000 |
| 39 | 40 | 65.000000 | 4 | 1955 | 1955 | 0.000 | 0 | 42 | 1152 | 0 | 1152 | 2 | 6 | 0 | 1978.506164 | 0 | 0 | 0 | 0 | 82000 |
| 40 | 41 | 84.000000 | 6 | 1965 | 1965 | 101.000 | 643 | 1088 | 1324 | 0 | 1324 | 2 | 6 | 1 | 1965.000000 | 2 | 440 | 0 | 138 | 160000 |
| 41 | 42 | 107.500000 | 5 | 1959 | 1959 | 0.000 | 967 | 1350 | 1328 | 0 | 1328 | 1 | 5 | 2 | 1959.000000 | 1 | 308 | 0 | 104 | 170000 |
| 42 | 43 | 70.049958 | 5 | 1983 | 1983 | 0.000 | 747 | 840 | 884 | 0 | 884 | 1 | 5 | 0 | 1983.000000 | 2 | 504 | 240 | 0 | 144000 |
| 43 | 44 | 70.049958 | 5 | 1975 | 1980 | 0.000 | 280 | 938 | 938 | 0 | 938 | 1 | 5 | 0 | 1977.000000 | 1 | 308 | 145 | 0 | 130250 |
| 44 | 45 | 70.000000 | 5 | 1959 | 1959 | 0.000 | 179 | 1150 | 1150 | 0 | 1150 | 1 | 6 | 0 | 1959.000000 | 1 | 300 | 0 | 0 | 141000 |
| 45 | 46 | 61.000000 | 9 | 2005 | 2005 | 410.625 | 456 | 1752 | 1752 | 0 | 1752 | 2 | 6 | 1 | 2005.000000 | 2 | 576 | 196 | 82 | 319900 |
| 46 | 47 | 48.000000 | 7 | 2003 | 2003 | 0.000 | 1351 | 1434 | 1518 | 631 | 2149 | 1 | 6 | 1 | 2003.000000 | 2 | 670 | 168 | 43 | 239686 |
| 47 | 48 | 84.000000 | 8 | 2006 | 2006 | 0.000 | 24 | 1656 | 1656 | 0 | 1656 | 2 | 7 | 0 | 2006.000000 | 3 | 826 | 0 | 146 | 249700 |
| 48 | 49 | 33.000000 | 4 | 1920 | 2008 | 0.000 | 0 | 736 | 736 | 716 | 1452 | 2 | 8 | 0 | 1978.506164 | 0 | 0 | 0 | 0 | 113000 |
| 49 | 50 | 66.000000 | 5 | 1966 | 1966 | 0.000 | 763 | 955 | 955 | 0 | 955 | 1 | 6 | 0 | 1966.000000 | 1 | 386 | 0 | 0 | 127000 |
skewValue = df2_n.skew(axis=0, numeric_only=True)
print(skewValue)
Id 0.000000 LotFrontage 0.061947 OverallQual 0.249513 YearBuilt -0.594020 YearRemodAdd -0.503562 MasVnrArea 1.281293 BsmtFinSF1 0.739746 TotalBsmtSF 0.237240 1stFlrSF 0.664066 2ndFlrSF 0.800109 GrLivArea 0.593212 FullBath 0.036562 TotRmsAbvGrd 0.366919 Fireplaces 0.550521 GarageYrBlt -0.666040 GarageCars -0.418495 GarageArea -0.069907 WoodDeckSF 1.083673 OpenPorchSF 1.136301 SalePrice 0.802784 dtype: float64
sns.pairplot(df2_n)
<seaborn.axisgrid.PairGrid at 0x7fcacfd07c70>
df_c.head()
| Id | MSZoning | Street | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinType2 | Heating | HeatingQC | CentralAir | Electrical | KitchenQual | Functional | FireplaceQu | GarageType | GarageFinish | GarageQual | GarageCond | PavedDrive | SaleType | SaleCondition | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | RL | Pave | Reg | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | No | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | NaN | Attchd | RFn | TA | TA | Y | WD | Normal | 208500 |
| 1 | 2 | RL | Pave | Reg | Lvl | AllPub | FR2 | Gtl | Veenker | Feedr | Norm | 1Fam | 1Story | Gable | CompShg | MetalSd | MetalSd | None | TA | TA | CBlock | Gd | TA | Gd | ALQ | Unf | GasA | Ex | Y | SBrkr | TA | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 181500 |
| 2 | 3 | RL | Pave | IR1 | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | Mn | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 223500 |
| 3 | 4 | RL | Pave | IR1 | Lvl | AllPub | Corner | Gtl | Crawfor | Norm | Norm | 1Fam | 2Story | Gable | CompShg | Wd Sdng | Wd Shng | None | TA | TA | BrkTil | TA | Gd | No | ALQ | Unf | GasA | Gd | Y | SBrkr | Gd | Typ | Gd | Detchd | Unf | TA | TA | Y | WD | Abnorml | 140000 |
| 4 | 5 | RL | Pave | IR1 | Lvl | AllPub | FR2 | Gtl | NoRidge | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | Av | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 250000 |
df.head()
| Id | MSSubClass | MSZoning | LotFrontage | LotArea | Street | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | OverallQual | OverallCond | YearBuilt | YearRemodAdd | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | MasVnrArea | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinSF1 | BsmtFinType2 | BsmtFinSF2 | BsmtUnfSF | TotalBsmtSF | Heating | HeatingQC | CentralAir | Electrical | 1stFlrSF | 2ndFlrSF | LowQualFinSF | GrLivArea | BsmtFullBath | BsmtHalfBath | FullBath | HalfBath | Bedroom | Kitchen | KitchenQual | TotRmsAbvGrd | Functional | Fireplaces | FireplaceQu | GarageType | GarageYrBlt | GarageFinish | GarageCars | GarageArea | GarageQual | GarageCond | PavedDrive | WoodDeckSF | OpenPorchSF | EnclosedPorch | 3SsnPorch | ScreenPorch | PoolArea | MiscVal | MoSold | YrSold | SaleType | SaleCondition | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 60 | RL | 65.0 | 8450 | Pave | Reg | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | 7 | 5 | 2003 | 2003 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 196.0 | Gd | TA | PConc | Gd | TA | No | GLQ | 706 | Unf | 0 | 150 | 856 | GasA | Ex | Y | SBrkr | 856 | 854 | 0 | 1710 | 1 | 0 | 2 | 1 | 3 | 1 | Gd | 8 | Typ | 0 | NaN | Attchd | 2003.0 | RFn | 2 | 548 | TA | TA | Y | 0 | 61 | 0 | 0 | 0 | 0 | 0 | 2 | 2008 | WD | Normal | 208500 |
| 1 | 2 | 20 | RL | 80.0 | 9600 | Pave | Reg | Lvl | AllPub | FR2 | Gtl | Veenker | Feedr | Norm | 1Fam | 1Story | 6 | 8 | 1976 | 1976 | Gable | CompShg | MetalSd | MetalSd | None | 0.0 | TA | TA | CBlock | Gd | TA | Gd | ALQ | 978 | Unf | 0 | 284 | 1262 | GasA | Ex | Y | SBrkr | 1262 | 0 | 0 | 1262 | 0 | 1 | 2 | 0 | 3 | 1 | TA | 6 | Typ | 1 | TA | Attchd | 1976.0 | RFn | 2 | 460 | TA | TA | Y | 298 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 2007 | WD | Normal | 181500 |
| 2 | 3 | 60 | RL | 68.0 | 11250 | Pave | IR1 | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | 7 | 5 | 2001 | 2002 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 162.0 | Gd | TA | PConc | Gd | TA | Mn | GLQ | 486 | Unf | 0 | 434 | 920 | GasA | Ex | Y | SBrkr | 920 | 866 | 0 | 1786 | 1 | 0 | 2 | 1 | 3 | 1 | Gd | 6 | Typ | 1 | TA | Attchd | 2001.0 | RFn | 2 | 608 | TA | TA | Y | 0 | 42 | 0 | 0 | 0 | 0 | 0 | 9 | 2008 | WD | Normal | 223500 |
| 3 | 4 | 70 | RL | 60.0 | 9550 | Pave | IR1 | Lvl | AllPub | Corner | Gtl | Crawfor | Norm | Norm | 1Fam | 2Story | 7 | 5 | 1915 | 1970 | Gable | CompShg | Wd Sdng | Wd Shng | None | 0.0 | TA | TA | BrkTil | TA | Gd | No | ALQ | 216 | Unf | 0 | 540 | 756 | GasA | Gd | Y | SBrkr | 961 | 756 | 0 | 1717 | 1 | 0 | 1 | 0 | 3 | 1 | Gd | 7 | Typ | 1 | Gd | Detchd | 1998.0 | Unf | 3 | 642 | TA | TA | Y | 0 | 35 | 272 | 0 | 0 | 0 | 0 | 2 | 2006 | WD | Abnorml | 140000 |
| 4 | 5 | 60 | RL | 84.0 | 14260 | Pave | IR1 | Lvl | AllPub | FR2 | Gtl | NoRidge | Norm | Norm | 1Fam | 2Story | 8 | 5 | 2000 | 2000 | Gable | CompShg | VinylSd | VinylSd | BrkFace | 350.0 | Gd | TA | PConc | Gd | TA | Av | GLQ | 655 | Unf | 0 | 490 | 1145 | GasA | Ex | Y | SBrkr | 1145 | 1053 | 0 | 2198 | 1 | 0 | 2 | 1 | 4 | 1 | Gd | 9 | Typ | 1 | TA | Attchd | 2000.0 | RFn | 3 | 836 | TA | TA | Y | 192 | 84 | 0 | 0 | 0 | 0 | 0 | 12 | 2008 | WD | Normal | 250000 |
for col in df_c:
print('***** ' + col + ' count= ' + str(len(df_c[col].unique())) + ' ******')
print(df_c[col].unique())
***** Id count= 1460 ****** [ 1 2 3 ... 1458 1459 1460] ***** MSZoning count= 5 ****** ['RL' 'RM' 'C (all)' 'FV' 'RH'] ***** Street count= 2 ****** ['Pave' 'Grvl'] ***** LotShape count= 4 ****** ['Reg' 'IR1' 'IR2' 'IR3'] ***** LandContour count= 4 ****** ['Lvl' 'Bnk' 'Low' 'HLS'] ***** Utilities count= 2 ****** ['AllPub' 'NoSeWa'] ***** LotConfig count= 5 ****** ['Inside' 'FR2' 'Corner' 'CulDSac' 'FR3'] ***** LandSlope count= 3 ****** ['Gtl' 'Mod' 'Sev'] ***** Neighborhood count= 25 ****** ['CollgCr' 'Veenker' 'Crawfor' 'NoRidge' 'Mitchel' 'Somerst' 'NWAmes' 'OldTown' 'BrkSide' 'Sawyer' 'NridgHt' 'mes' 'SawyerW' 'IDOTRR' 'MeadowV' 'Edwards' 'Timber' 'Gilbert' 'StoneBr' 'ClearCr' 'NPkVill' 'Blmngtn' 'BrDale' 'SWISU' 'Blueste'] ***** Condition1 count= 9 ****** ['Norm' 'Feedr' 'PosN' 'Artery' 'RRAe' 'RRNn' 'RRAn' 'PosA' 'RRNe'] ***** Condition2 count= 8 ****** ['Norm' 'Artery' 'RRNn' 'Feedr' 'PosN' 'PosA' 'RRAn' 'RRAe'] ***** BldgType count= 5 ****** ['1Fam' '2fmCon' 'Duplex' 'TwnhsE' 'Twnhs'] ***** HouseStyle count= 8 ****** ['2Story' '1Story' '1.5Fin' '1.5Unf' 'SFoyer' 'SLvl' '2.5Unf' '2.5Fin'] ***** RoofStyle count= 6 ****** ['Gable' 'Hip' 'Gambrel' 'Mansard' 'Flat' 'Shed'] ***** RoofMatl count= 8 ****** ['CompShg' 'WdShngl' 'Metal' 'WdShake' 'Membran' 'Tar&Grv' 'Roll' 'ClyTile'] ***** Exterior1st count= 15 ****** ['VinylSd' 'MetalSd' 'Wd Sdng' 'HdBoard' 'BrkFace' 'WdShing' 'CemntBd' 'Plywood' 'AsbShng' 'Stucco' 'BrkComm' 'AsphShn' 'Stone' 'ImStucc' 'CBlock'] ***** Exterior2nd count= 16 ****** ['VinylSd' 'MetalSd' 'Wd Shng' 'HdBoard' 'Plywood' 'Wd Sdng' 'CmentBd' 'BrkFace' 'Stucco' 'AsbShng' 'Brk Cmn' 'ImStucc' 'AsphShn' 'Stone' 'Other' 'CBlock'] ***** MasVnrType count= 5 ****** ['BrkFace' 'None' 'Stone' 'BrkCmn' nan] ***** ExterQual count= 4 ****** ['Gd' 'TA' 'Ex' 'Fa'] ***** ExterCond count= 5 ****** ['TA' 'Gd' 'Fa' 'Po' 'Ex'] ***** Foundation count= 6 ****** ['PConc' 'CBlock' 'BrkTil' 'Wood' 'Slab' 'Stone'] ***** BsmtQual count= 5 ****** ['Gd' 'TA' 'Ex' nan 'Fa'] ***** BsmtCond count= 5 ****** ['TA' 'Gd' nan 'Fa' 'Po'] ***** BsmtExposure count= 5 ****** ['No' 'Gd' 'Mn' 'Av' nan] ***** BsmtFinType1 count= 7 ****** ['GLQ' 'ALQ' 'Unf' 'Rec' 'BLQ' nan 'LwQ'] ***** BsmtFinType2 count= 7 ****** ['Unf' 'BLQ' nan 'ALQ' 'Rec' 'LwQ' 'GLQ'] ***** Heating count= 6 ****** ['GasA' 'GasW' 'Grav' 'Wall' 'OthW' 'Floor'] ***** HeatingQC count= 5 ****** ['Ex' 'Gd' 'TA' 'Fa' 'Po'] ***** CentralAir count= 2 ****** ['Y' 'N'] ***** Electrical count= 6 ****** ['SBrkr' 'FuseF' 'FuseA' 'FuseP' 'Mix' nan] ***** KitchenQual count= 4 ****** ['Gd' 'TA' 'Ex' 'Fa'] ***** Functional count= 7 ****** ['Typ' 'Min1' 'Maj1' 'Min2' 'Mod' 'Maj2' 'Sev'] ***** FireplaceQu count= 6 ****** [nan 'TA' 'Gd' 'Fa' 'Ex' 'Po'] ***** GarageType count= 7 ****** ['Attchd' 'Detchd' 'BuiltIn' 'CarPort' nan 'Basment' '2Types'] ***** GarageFinish count= 4 ****** ['RFn' 'Unf' 'Fin' nan] ***** GarageQual count= 6 ****** ['TA' 'Fa' 'Gd' nan 'Ex' 'Po'] ***** GarageCond count= 6 ****** ['TA' 'Fa' nan 'Gd' 'Po' 'Ex'] ***** PavedDrive count= 3 ****** ['Y' 'N' 'P'] ***** SaleType count= 9 ****** ['WD' 'New' 'COD' 'ConLD' 'ConLI' 'CWD' 'ConLw' 'Con' 'Oth'] ***** SaleCondition count= 6 ****** ['Normal' 'Abnorml' 'Partial' 'AdjLand' 'Alloca' 'Family'] ***** SalePrice count= 663 ****** [208500 181500 223500 140000 250000 143000 307000 200000 129900 118000 129500 345000 144000 279500 157000 132000 149000 90000 159000 139000 325300 139400 230000 154000 256300 134800 306000 207500 68500 40000 149350 179900 165500 277500 309000 145000 153000 109000 82000 160000 170000 130250 141000 319900 239686 249700 113000 127000 177000 114500 110000 385000 130000 180500 172500 196500 438780 124900 158000 101000 202500 219500 317000 180000 226000 80000 225000 244000 185000 144900 107400 91000 135750 136500 193500 153500 245000 126500 168500 260000 174000 164500 85000 123600 109900 98600 163500 133900 204750 214000 94750 83000 128950 205000 178000 118964 198900 169500 100000 115000 190000 136900 383970 217000 259500 176000 155000 320000 163990 136000 153900 181000 84500 128000 87000 150000 150750 220000 171000 231500 166000 204000 125000 105000 222500 122000 372402 235000 79000 109500 269500 254900 162500 412500 103200 152000 127500 325624 183500 228000 128500 215000 239000 163000 184000 243000 211000 501837 200100 120000 475000 173000 135000 153337 286000 315000 192000 148500 311872 104000 274900 171500 112000 143900 277000 98000 186000 252678 156000 161750 134450 210000 107000 311500 167240 204900 97000 386250 290000 106000 192500 148000 403000 94500 128200 216500 89500 185500 194500 318000 262500 110500 241500 137000 76500 276000 151000 73000 175500 179500 120500 266000 124500 201000 415298 228500 244600 179200 164700 88000 153575 233230 135900 131000 167000 142500 175000 158500 267000 149900 295000 305900 82500 360000 165600 119900 375000 188500 270000 187500 342643 354000 301000 126175 242000 324000 145250 214500 78000 119000 284000 207000 228950 377426 202900 87500 140200 151500 157500 437154 318061 95000 105900 177500 134000 280000 198500 147000 165000 162000 172400 134432 123000 61000 340000 394432 179000 187750 213500 76000 240000 81000 191000 426000 106500 129000 67000 241000 245500 164990 108000 258000 168000 339750 60000 222000 181134 149500 126000 142000 206300 275000 109008 195400 85400 79900 122500 212000 116000 90350 555000 162900 199900 119500 188000 256000 161000 263435 62383 188700 124000 178740 146500 187000 440000 251000 132500 208900 380000 297000 89471 326000 374000 164000 86000 133000 172785 91300 34900 430000 226700 289000 208300 164900 202665 96500 402861 265000 234000 106250 184750 315750 446261 200624 107500 39300 111250 272000 248000 213250 179665 229000 263000 112500 255500 121500 268000 325000 316600 135960 142600 224500 118500 146000 131500 181900 253293 369900 79500 185900 451950 138000 319000 114504 194201 217500 221000 359100 313000 261500 75500 137500 183200 105500 314813 305000 165150 139900 209500 93000 264561 274000 370878 143250 98300 205950 350000 145500 97500 197900 402000 423000 230500 173500 103600 257500 372500 159434 285000 227875 148800 392000 194700 755000 335000 108480 141500 89000 123500 138500 196000 312500 361919 213000 55000 302000 254000 179540 52000 102776 189000 130500 159500 341000 103000 236500 131400 93500 239900 299800 236000 265979 260400 275500 158900 179400 215200 337000 264132 216837 538000 134900 102000 395000 221500 175900 187100 161500 233000 107900 160200 146800 269790 143500 485000 582933 227680 135500 159950 144500 55993 157900 224900 271000 224000 183000 139500 232600 147400 237000 139950 174900 133500 189950 250580 248900 169000 200500 66500 303477 132250 328900 122900 154500 118858 142953 611657 125500 255000 154300 173733 75000 35311 238000 176500 145900 169990 193000 117500 184900 253000 239799 244400 150900 197500 172000 116500 214900 178900 37900 99500 182000 167500 85500 178400 336000 159895 255900 117000 395192 195000 197000 348000 173900 337500 121600 206000 232000 136905 119200 227000 203000 213490 194000 287000 293077 310000 119750 84000 315500 262280 278000 139600 556581 84900 176485 200141 185850 328000 167900 151400 91500 138800 155900 83500 252000 92900 176432 274725 134500 184100 133700 118400 212900 163900 259000 239500 94000 424870 174500 116900 201800 218000 235128 108959 233170 245350 625000 171900 154900 392500 745000 186700 104900 262000 219210 116050 271900 229456 80500 137900 367294 101800 138887 265900 248328 465000 186500 169900 171750 294000 165400 301500 99900 128900 183900 378500 381000 185750 68400 150500 281000 333168 206900 295493 111000 156500 72500 52500 155835 108500 283463 410000 156932 144152 216000 274300 466500 58500 237500 377500 246578 281213 137450 193879 282922 257000 223000 274970 182900 192140 143750 64500 394617 149700 149300 121000 179600 92000 287090 266500 142125 147500]
null_columns = df_c.isna().sum()
null_columns = null_columns[null_columns > 0]
null_columns
MasVnrType 8 BsmtQual 37 BsmtCond 37 BsmtExposure 38 BsmtFinType1 37 BsmtFinType2 38 Electrical 1 FireplaceQu 690 GarageType 81 GarageFinish 81 GarageQual 81 GarageCond 81 dtype: int64
df_clean = df_c.apply(lambda x: x.fillna(x.value_counts().index[0]))
df_clean.head()
| Id | MSZoning | Street | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinType2 | Heating | HeatingQC | CentralAir | Electrical | KitchenQual | Functional | FireplaceQu | GarageType | GarageFinish | GarageQual | GarageCond | PavedDrive | SaleType | SaleCondition | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | RL | Pave | Reg | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | No | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | Gd | Attchd | RFn | TA | TA | Y | WD | Normal | 208500 |
| 1 | 2 | RL | Pave | Reg | Lvl | AllPub | FR2 | Gtl | Veenker | Feedr | Norm | 1Fam | 1Story | Gable | CompShg | MetalSd | MetalSd | None | TA | TA | CBlock | Gd | TA | Gd | ALQ | Unf | GasA | Ex | Y | SBrkr | TA | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 181500 |
| 2 | 3 | RL | Pave | IR1 | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | Mn | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 223500 |
| 3 | 4 | RL | Pave | IR1 | Lvl | AllPub | Corner | Gtl | Crawfor | Norm | Norm | 1Fam | 2Story | Gable | CompShg | Wd Sdng | Wd Shng | None | TA | TA | BrkTil | TA | Gd | No | ALQ | Unf | GasA | Gd | Y | SBrkr | Gd | Typ | Gd | Detchd | Unf | TA | TA | Y | WD | Abnorml | 140000 |
| 4 | 5 | RL | Pave | IR1 | Lvl | AllPub | FR2 | Gtl | NoRidge | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | Av | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 250000 |
df_c = df_clean.copy()
null_columns = df_clean.isna().sum()
null_columns = null_columns[null_columns > 0]
null_columns
Series([], dtype: int64)
df_c.head()
| Id | MSZoning | Street | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinType2 | Heating | HeatingQC | CentralAir | Electrical | KitchenQual | Functional | FireplaceQu | GarageType | GarageFinish | GarageQual | GarageCond | PavedDrive | SaleType | SaleCondition | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | RL | Pave | Reg | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | No | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | Gd | Attchd | RFn | TA | TA | Y | WD | Normal | 208500 |
| 1 | 2 | RL | Pave | Reg | Lvl | AllPub | FR2 | Gtl | Veenker | Feedr | Norm | 1Fam | 1Story | Gable | CompShg | MetalSd | MetalSd | None | TA | TA | CBlock | Gd | TA | Gd | ALQ | Unf | GasA | Ex | Y | SBrkr | TA | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 181500 |
| 2 | 3 | RL | Pave | IR1 | Lvl | AllPub | Inside | Gtl | CollgCr | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | Mn | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 223500 |
| 3 | 4 | RL | Pave | IR1 | Lvl | AllPub | Corner | Gtl | Crawfor | Norm | Norm | 1Fam | 2Story | Gable | CompShg | Wd Sdng | Wd Shng | None | TA | TA | BrkTil | TA | Gd | No | ALQ | Unf | GasA | Gd | Y | SBrkr | Gd | Typ | Gd | Detchd | Unf | TA | TA | Y | WD | Abnorml | 140000 |
| 4 | 5 | RL | Pave | IR1 | Lvl | AllPub | FR2 | Gtl | NoRidge | Norm | Norm | 1Fam | 2Story | Gable | CompShg | VinylSd | VinylSd | BrkFace | Gd | TA | PConc | Gd | TA | Av | GLQ | Unf | GasA | Ex | Y | SBrkr | Gd | Typ | TA | Attchd | RFn | TA | TA | Y | WD | Normal | 250000 |
def histPlotLoop(df, columns):
for col in columns:
if (df[col].dtype == object) :
i = df_c[col].unique()
i.sort()
plt.figure(figsize=(16,6))
ax = sns.countplot(x=col, data=df, order=i)
ax.set_xticklabels(ax.get_xticklabels(), rotation=40, ha="right", fontsize=8)
plt.title(col, fontsize=14, fontweight='bold')
plt.show()
histPlotLoop(df_c, df_c.columns)
import category_encoders as ce
encoder = ce.ordinal.OrdinalEncoder(return_df = True)
df_train = encoder.fit_transform(df_c)
df_train.head()
| Id | MSZoning | Street | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinType2 | Heating | HeatingQC | CentralAir | Electrical | KitchenQual | Functional | FireplaceQu | GarageType | GarageFinish | GarageQual | GarageCond | PavedDrive | SaleType | SaleCondition | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 208500 |
| 1 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 1 | 2 | 1 | 1 | 2 | 2 | 2 | 2 | 1 | 2 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 181500 |
| 2 | 3 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 223500 |
| 3 | 4 | 1 | 1 | 2 | 1 | 1 | 3 | 1 | 3 | 1 | 1 | 1 | 1 | 1 | 1 | 3 | 3 | 2 | 2 | 1 | 3 | 2 | 2 | 1 | 2 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 2 | 140000 |
| 4 | 5 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 250000 |
df_c = df_train
df_c.corr()
| Id | MSZoning | Street | LotShape | LandContour | Utilities | LotConfig | LandSlope | Neighborhood | Condition1 | Condition2 | BldgType | HouseStyle | RoofStyle | RoofMatl | Exterior1st | Exterior2nd | MasVnrType | ExterQual | ExterCond | Foundation | BsmtQual | BsmtCond | BsmtExposure | BsmtFinType1 | BsmtFinType2 | Heating | HeatingQC | CentralAir | Electrical | KitchenQual | Functional | FireplaceQu | GarageType | GarageFinish | GarageQual | GarageCond | PavedDrive | SaleType | SaleCondition | SalePrice | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Id | 1.000000 | -0.011949 | -0.008916 | -0.024071 | 0.014769 | 0.013324 | -0.038192 | 0.005847 | -0.013078 | -0.010735 | -0.016961 | 0.019230 | 0.026166 | 0.015873 | 0.013375 | 0.003624 | 0.015449 | 0.024866 | -0.021844 | -0.037865 | 0.020023 | -0.020092 | -0.013579 | 0.031591 | 0.012783 | 0.011692 | 0.061902 | 0.015001 | -0.009821 | -0.048029 | -0.020927 | 0.006018 | -0.024325 | 0.009743 | -0.002604 | -0.014586 | 0.000987 | 0.008921 | -0.016932 | -0.017712 | -0.021917 |
| MSZoning | -0.011949 | 1.000000 | 0.039678 | -0.167918 | -0.013396 | -0.011167 | -0.094747 | -0.043444 | -0.067889 | -0.026021 | 0.016014 | 0.271750 | -0.047199 | -0.088537 | -0.049864 | -0.063732 | -0.025637 | 0.069241 | -0.018093 | 0.017808 | 0.011181 | -0.015100 | 0.059041 | -0.069148 | 0.015065 | -0.083130 | 0.012889 | -0.035643 | 0.144970 | 0.084804 | -0.030523 | -0.029668 | -0.117721 | 0.099439 | 0.005704 | 0.039895 | 0.028618 | 0.117402 | 0.058948 | 0.068705 | -0.116047 |
| Street | -0.008916 | 0.039678 | 1.000000 | 0.010129 | 0.097236 | -0.001682 | 0.004458 | 0.179360 | 0.053445 | 0.023914 | -0.005881 | 0.023392 | 0.025328 | 0.006881 | -0.007749 | 0.011874 | 0.015314 | -0.013980 | 0.143047 | 0.002633 | 0.021128 | -0.003653 | -0.017284 | 0.045421 | 0.019357 | 0.041977 | -0.008194 | 0.043211 | 0.069869 | 0.021466 | 0.055746 | -0.015465 | -0.017230 | 0.135136 | -0.010497 | -0.012414 | -0.010851 | 0.011248 | 0.021171 | 0.022919 | -0.041036 |
| LotShape | -0.024071 | -0.167918 | 0.010129 | 1.000000 | 0.201047 | 0.026616 | 0.308073 | 0.144248 | -0.013295 | 0.066612 | 0.052183 | -0.134373 | -0.067647 | 0.043063 | 0.118271 | -0.030630 | -0.068168 | 0.034001 | -0.111502 | -0.047015 | -0.158779 | -0.117374 | -0.010689 | 0.106951 | -0.088367 | 0.047531 | -0.043320 | -0.119556 | -0.099138 | -0.086719 | -0.089061 | -0.017841 | 0.102891 | -0.045583 | 0.067334 | -0.046539 | -0.065380 | -0.100666 | 0.001435 | -0.004773 | 0.267759 |
| LandContour | 0.014769 | -0.013396 | 0.097236 | 0.201047 | 1.000000 | -0.007963 | 0.021107 | 0.507203 | 0.113336 | -0.021478 | 0.012096 | -0.042667 | -0.004987 | 0.100625 | 0.109717 | 0.034502 | 0.014566 | 0.086651 | 0.075493 | 0.001553 | -0.000145 | 0.019828 | 0.009301 | 0.078411 | 0.010181 | -0.024136 | -0.024849 | 0.028243 | 0.023605 | 0.021122 | 0.037246 | 0.016088 | -0.053251 | 0.032066 | 0.055327 | -0.030841 | -0.022491 | 0.081031 | -0.014537 | 0.062826 | 0.092009 |
| Utilities | 0.013324 | -0.011167 | -0.001682 | 0.026616 | -0.007963 | 1.000000 | 0.062298 | -0.005909 | 0.029258 | -0.008311 | -0.002397 | -0.010899 | 0.076219 | -0.011462 | -0.003158 | 0.008321 | 0.004602 | -0.033471 | 0.012733 | -0.008842 | 0.008611 | 0.010654 | -0.007044 | -0.016287 | -0.030603 | 0.068449 | -0.003339 | 0.003963 | -0.006907 | 0.091048 | -0.026715 | -0.006303 | -0.012962 | 0.114822 | -0.034307 | -0.005059 | -0.004422 | -0.007368 | 0.057455 | 0.019173 | -0.014314 |
| LotConfig | -0.038192 | -0.094747 | 0.004458 | 0.308073 | 0.021107 | 0.062298 | 1.000000 | 0.033673 | -0.055593 | 0.061695 | 0.018440 | -0.106812 | 0.005696 | 0.067337 | 0.112297 | 0.022409 | 0.015406 | -0.004082 | -0.037293 | 0.008542 | 0.000844 | 0.009188 | 0.034666 | 0.065806 | -0.000638 | 0.022254 | 0.004421 | -0.018754 | -0.026600 | -0.038397 | -0.046671 | 0.008194 | 0.057436 | -0.006936 | 0.029645 | 0.006178 | 0.034839 | -0.065917 | 0.012120 | -0.013638 | 0.109106 |
| LandSlope | 0.005847 | -0.043444 | 0.179360 | 0.144248 | 0.507203 | -0.005909 | 0.033673 | 1.000000 | 0.072891 | -0.020333 | -0.014034 | -0.056801 | 0.018446 | 0.189081 | 0.221080 | 0.110339 | 0.093730 | 0.066658 | 0.092514 | 0.002675 | 0.044388 | 0.002120 | 0.017945 | 0.083769 | 0.018924 | 0.084262 | 0.003625 | 0.057444 | 0.010849 | 0.013048 | 0.029612 | 0.097882 | 0.013180 | 0.063664 | 0.018310 | -0.014761 | -0.008288 | 0.018009 | -0.056680 | 0.017187 | 0.051152 |
| Neighborhood | -0.013078 | -0.067889 | 0.053445 | -0.013295 | 0.113336 | 0.029258 | -0.055593 | 0.072891 | 1.000000 | 0.002747 | -0.012207 | 0.184198 | 0.060100 | 0.061029 | 0.091511 | 0.102043 | 0.121032 | 0.025252 | 0.176707 | -0.018067 | 0.104070 | 0.090658 | 0.075323 | -0.081853 | 0.136957 | 0.018894 | 0.000671 | 0.130972 | 0.070703 | 0.070396 | 0.153756 | 0.077679 | 0.011046 | 0.088574 | 0.175353 | -0.030853 | 0.006989 | 0.065998 | 0.008466 | 0.007101 | -0.143621 |
| Condition1 | -0.010735 | -0.026021 | 0.023914 | 0.066612 | -0.021478 | -0.008311 | 0.061695 | -0.020333 | 0.002747 | 1.000000 | 0.189172 | -0.088630 | -0.026940 | 0.008366 | 0.046832 | 0.027841 | 0.006597 | 0.014508 | 0.057713 | 0.035770 | 0.057340 | 0.052677 | 0.008640 | -0.053324 | 0.047064 | 0.026240 | 0.009515 | 0.048060 | 0.040365 | 0.042287 | 0.050975 | -0.003683 | -0.012094 | 0.021200 | 0.026413 | 0.006257 | 0.002639 | 0.034886 | 0.003128 | -0.019800 | -0.044820 |
| Condition2 | -0.016961 | 0.016014 | -0.005881 | 0.052183 | 0.012096 | -0.002397 | 0.018440 | -0.014034 | -0.012207 | 0.189172 | 1.000000 | 0.009902 | 0.025958 | 0.097614 | -0.011044 | 0.067090 | 0.037421 | 0.011156 | 0.105079 | 0.098064 | 0.049946 | 0.067830 | 0.028978 | -0.018015 | -0.012587 | 0.018532 | -0.011678 | 0.031041 | 0.035238 | -0.006245 | 0.062163 | -0.022040 | -0.025387 | 0.064196 | 0.008562 | 0.062199 | 0.006563 | 0.054484 | 0.011960 | 0.005158 | -0.004833 |
| BldgType | 0.019230 | 0.271750 | 0.023392 | -0.134373 | -0.042667 | -0.010899 | -0.106812 | -0.056801 | 0.184198 | -0.088630 | 0.009902 | 1.000000 | -0.058511 | -0.069012 | -0.024057 | 0.074220 | 0.035336 | -0.052490 | -0.082211 | -0.073555 | -0.082327 | -0.142979 | -0.038452 | 0.014987 | -0.127092 | -0.008480 | -0.004899 | 0.007516 | -0.008399 | -0.064550 | -0.046908 | -0.040279 | -0.042824 | 0.013266 | 0.016879 | -0.048735 | -0.056821 | -0.064447 | 0.044289 | 0.040365 | -0.112611 |
| HouseStyle | 0.026166 | -0.047199 | 0.025328 | -0.067647 | -0.004987 | 0.076219 | 0.005696 | 0.018446 | 0.060100 | -0.026940 | 0.025958 | -0.058511 | 1.000000 | 0.005490 | 0.042447 | 0.121359 | 0.137919 | -0.025943 | 0.188102 | 0.083020 | 0.189020 | 0.048008 | 0.034996 | 0.169969 | 0.018787 | 0.055591 | 0.063811 | 0.144855 | 0.061378 | 0.058579 | 0.109841 | 0.036488 | -0.016011 | 0.112935 | -0.029816 | 0.073689 | 0.065567 | 0.038707 | -0.006154 | -0.029049 | -0.188688 |
| RoofStyle | 0.015873 | -0.088537 | 0.006881 | 0.043063 | 0.100625 | -0.011462 | 0.067337 | 0.189081 | 0.061029 | 0.008366 | 0.097614 | -0.069012 | 0.005490 | 1.000000 | 0.509733 | 0.117708 | 0.115663 | -0.018066 | 0.090164 | 0.040420 | 0.017324 | 0.117402 | 0.032465 | 0.012399 | 0.007190 | 0.076391 | 0.002689 | 0.007909 | 0.011614 | -0.036031 | 0.086051 | 0.074203 | 0.036268 | -0.006125 | 0.072046 | -0.006440 | 0.050651 | 0.011743 | -0.031824 | 0.032990 | 0.159332 |
| RoofMatl | 0.013375 | -0.049864 | -0.007749 | 0.118271 | 0.109717 | -0.003158 | 0.112297 | 0.221080 | 0.091511 | 0.046832 | -0.011044 | -0.024057 | 0.042447 | 0.509733 | 1.000000 | 0.195398 | 0.116622 | 0.047836 | 0.021100 | 0.028508 | 0.030506 | -0.025116 | 0.029506 | 0.021721 | -0.003143 | 0.053925 | 0.005356 | 0.034147 | -0.006083 | 0.005184 | 0.033353 | 0.096702 | 0.047445 | 0.012190 | 0.028933 | 0.024703 | 0.059823 | -0.033947 | -0.014712 | 0.055436 | 0.035820 |
| Exterior1st | 0.003624 | -0.063732 | 0.011874 | -0.030630 | 0.034502 | 0.008321 | 0.022409 | 0.110339 | 0.102043 | 0.027841 | 0.067090 | 0.074220 | 0.121359 | 0.117708 | 0.195398 | 1.000000 | 0.749599 | 0.001471 | 0.253995 | 0.053318 | 0.289169 | 0.072958 | -0.003168 | -0.065751 | 0.123582 | 0.164247 | 0.078936 | 0.343369 | 0.110442 | 0.082794 | 0.188481 | 0.062031 | 0.104904 | 0.020894 | 0.014384 | 0.043935 | 0.051225 | 0.058754 | -0.015554 | 0.000631 | -0.120586 |
| Exterior2nd | 0.015449 | -0.025637 | 0.015314 | -0.068168 | 0.014566 | 0.004602 | 0.015406 | 0.093730 | 0.121032 | 0.006597 | 0.037421 | 0.035336 | 0.137919 | 0.115663 | 0.116622 | 0.749599 | 1.000000 | 0.035522 | 0.275078 | 0.046500 | 0.339574 | 0.150843 | 0.047921 | -0.110260 | 0.176651 | 0.135464 | 0.127524 | 0.341511 | 0.209144 | 0.171184 | 0.209612 | 0.097594 | 0.042182 | 0.061058 | 0.013905 | 0.101342 | 0.085423 | 0.119255 | -0.033452 | -0.039999 | -0.164716 |
| MasVnrType | 0.024866 | 0.069241 | -0.013980 | 0.034001 | 0.086651 | -0.033471 | -0.004082 | 0.066658 | 0.025252 | 0.014508 | 0.011156 | -0.052490 | -0.025943 | -0.018066 | 0.047836 | 0.001471 | 0.035522 | 1.000000 | 0.043245 | 0.018964 | 0.034848 | 0.115350 | 0.025659 | 0.023652 | 0.040224 | 0.013607 | 0.022095 | -0.018947 | 0.056008 | 0.046910 | 0.083036 | 0.084426 | -0.119612 | 0.058091 | 0.040864 | 0.031918 | 0.033172 | 0.082059 | 0.071044 | 0.106159 | -0.010695 |
| ExterQual | -0.021844 | -0.018093 | 0.143047 | -0.111502 | 0.075493 | 0.012733 | -0.037293 | 0.092514 | 0.176707 | 0.057713 | 0.105079 | -0.082211 | 0.188102 | 0.090164 | 0.021100 | 0.253995 | 0.275078 | 0.043245 | 1.000000 | 0.153432 | 0.366565 | 0.389251 | 0.107594 | -0.149535 | 0.255477 | 0.087775 | 0.062039 | 0.380002 | 0.195975 | 0.183732 | 0.528378 | 0.098799 | -0.017219 | 0.135777 | 0.063070 | 0.031578 | 0.053477 | 0.166263 | 0.022013 | -0.009670 | -0.265015 |
| ExterCond | -0.037865 | 0.017808 | 0.002633 | -0.047015 | 0.001553 | -0.008842 | 0.008542 | 0.002675 | -0.018067 | 0.035770 | 0.098064 | -0.073555 | 0.083020 | 0.040420 | 0.028508 | 0.053318 | 0.046500 | 0.018964 | 0.153432 | 1.000000 | 0.206618 | 0.091963 | 0.212929 | -0.065298 | 0.081530 | -0.014319 | 0.033196 | 0.055997 | 0.112829 | 0.053171 | 0.098395 | 0.098871 | -0.012548 | 0.022384 | 0.003008 | 0.093184 | 0.059977 | 0.139432 | 0.003909 | -0.038491 | -0.121706 |
| Foundation | 0.020023 | 0.011181 | 0.021128 | -0.158779 | -0.000145 | 0.008611 | 0.000844 | 0.044388 | 0.104070 | 0.057340 | 0.049946 | -0.082327 | 0.189020 | 0.017324 | 0.030506 | 0.289169 | 0.339574 | 0.034848 | 0.366565 | 0.206618 | 1.000000 | 0.258424 | 0.112632 | -0.194213 | 0.329651 | 0.045337 | 0.238561 | 0.419451 | 0.327406 | 0.241477 | 0.270250 | 0.178060 | -0.043634 | 0.155160 | -0.054610 | 0.181056 | 0.144817 | 0.243846 | -0.050491 | -0.093201 | -0.429678 |
| BsmtQual | -0.020092 | -0.015100 | -0.003653 | -0.117374 | 0.019828 | 0.010654 | 0.009188 | 0.002120 | 0.090658 | 0.052677 | 0.067830 | -0.142979 | 0.048008 | 0.117402 | -0.025116 | 0.072958 | 0.150843 | 0.115350 | 0.389251 | 0.091963 | 0.258424 | 1.000000 | 0.144371 | -0.087171 | 0.191781 | 0.002272 | 0.020770 | 0.125190 | 0.134404 | 0.193564 | 0.377461 | 0.079358 | -0.054100 | 0.096753 | 0.105632 | 0.142403 | 0.140402 | 0.143237 | 0.029998 | 0.057392 | -0.004053 |
| BsmtCond | -0.013579 | 0.059041 | -0.017284 | -0.010689 | 0.009301 | -0.007044 | 0.034666 | 0.017945 | 0.075323 | 0.008640 | 0.028978 | -0.038452 | 0.034996 | 0.032465 | 0.029506 | -0.003168 | 0.047921 | 0.025659 | 0.107594 | 0.212929 | 0.112632 | 0.144371 | 1.000000 | -0.038548 | 0.096008 | -0.001153 | 0.063961 | 0.089517 | 0.179716 | 0.166612 | 0.115948 | 0.140345 | -0.046763 | 0.058767 | 0.024657 | 0.069530 | 0.165657 | 0.116541 | 0.060624 | -0.004425 | -0.082016 |
| BsmtExposure | 0.031591 | -0.069148 | 0.045421 | 0.106951 | 0.078411 | -0.016287 | 0.065806 | 0.083769 | -0.081853 | -0.053324 | -0.018015 | 0.014987 | 0.169969 | 0.012399 | 0.021721 | -0.065751 | -0.110260 | 0.023652 | -0.149535 | -0.065298 | -0.194213 | -0.087171 | -0.038548 | 1.000000 | -0.211143 | 0.029815 | -0.059522 | -0.139312 | -0.085375 | -0.119133 | -0.070103 | -0.072731 | 0.003477 | -0.039763 | 0.015157 | -0.047842 | -0.033896 | -0.070336 | 0.043161 | 0.094537 | 0.229632 |
| BsmtFinType1 | 0.012783 | 0.015065 | 0.019357 | -0.088367 | 0.010181 | -0.030603 | -0.000638 | 0.018924 | 0.136957 | 0.047064 | -0.012587 | -0.127092 | 0.018787 | 0.007190 | -0.003143 | 0.123582 | 0.176651 | 0.040224 | 0.255477 | 0.081530 | 0.329651 | 0.191781 | 0.096008 | -0.211143 | 1.000000 | 0.172309 | 0.066529 | 0.303916 | 0.123151 | 0.190449 | 0.180232 | 0.130886 | -0.075103 | 0.102446 | -0.051446 | 0.044489 | 0.045777 | 0.114083 | -0.017829 | -0.067683 | -0.360407 |
| BsmtFinType2 | 0.011692 | -0.083130 | 0.041977 | 0.047531 | -0.024136 | 0.068449 | 0.022254 | 0.084262 | 0.018894 | 0.026240 | 0.018532 | -0.008480 | 0.055591 | 0.076391 | 0.053925 | 0.164247 | 0.135464 | 0.013607 | 0.087775 | -0.014319 | 0.045337 | 0.002272 | -0.001153 | 0.029815 | 0.172309 | 1.000000 | -0.019777 | 0.112530 | -0.045441 | -0.044401 | 0.011502 | 0.073947 | 0.075770 | -0.009329 | -0.030182 | -0.004602 | 0.025548 | -0.077327 | -0.008694 | -0.060118 | -0.046649 |
| Heating | 0.061902 | 0.012889 | -0.008194 | -0.043320 | -0.024849 | -0.003339 | 0.004421 | 0.003625 | 0.000671 | 0.009515 | -0.011678 | -0.004899 | 0.063811 | 0.002689 | 0.005356 | 0.078936 | 0.127524 | 0.022095 | 0.062039 | 0.033196 | 0.238561 | 0.020770 | 0.063961 | -0.059522 | 0.066529 | -0.019777 | 1.000000 | 0.217619 | 0.429067 | 0.131262 | 0.123369 | 0.059807 | -0.029643 | 0.049661 | 0.008418 | 0.086007 | 0.133256 | 0.141864 | 0.018388 | -0.032650 | -0.106673 |
| HeatingQC | 0.015001 | -0.035643 | 0.043211 | -0.119556 | 0.028243 | 0.003963 | -0.018754 | 0.057444 | 0.130972 | 0.048060 | 0.031041 | 0.007516 | 0.144855 | 0.007909 | 0.034147 | 0.343369 | 0.341511 | -0.018947 | 0.380002 | 0.055997 | 0.419451 | 0.125190 | 0.089517 | -0.139312 | 0.303916 | 0.112530 | 0.217619 | 1.000000 | 0.306294 | 0.177931 | 0.299089 | 0.059352 | -0.009620 | 0.064946 | -0.056186 | 0.054182 | 0.090005 | 0.193215 | -0.009051 | -0.049721 | -0.427649 |
| CentralAir | -0.009821 | 0.144970 | 0.069869 | -0.099138 | 0.023605 | -0.006907 | -0.026600 | 0.010849 | 0.070703 | 0.040365 | 0.035238 | -0.008399 | 0.061378 | 0.011614 | -0.006083 | 0.110442 | 0.209144 | 0.056008 | 0.195975 | 0.112829 | 0.327406 | 0.134404 | 0.179716 | -0.085375 | 0.123151 | -0.045441 | 0.429067 | 0.306294 | 1.000000 | 0.294177 | 0.245980 | 0.099165 | -0.107931 | 0.116537 | 0.017410 | 0.150958 | 0.180947 | 0.275660 | 0.026522 | -0.010168 | -0.251328 |
| Electrical | -0.048029 | 0.084804 | 0.021466 | -0.086719 | 0.021122 | 0.091048 | -0.038397 | 0.013048 | 0.070396 | 0.042287 | -0.006245 | -0.064550 | 0.058579 | -0.036031 | 0.005184 | 0.082794 | 0.171184 | 0.046910 | 0.183732 | 0.053171 | 0.241477 | 0.193564 | 0.166612 | -0.119133 | 0.190449 | -0.044401 | 0.131262 | 0.177931 | 0.294177 | 1.000000 | 0.219022 | 0.093060 | -0.100106 | 0.109852 | -0.003678 | 0.138483 | 0.163950 | 0.175431 | -0.015363 | -0.022997 | -0.231417 |
| KitchenQual | -0.020927 | -0.030523 | 0.055746 | -0.089061 | 0.037246 | -0.026715 | -0.046671 | 0.029612 | 0.153756 | 0.050975 | 0.062163 | -0.046908 | 0.109841 | 0.086051 | 0.033353 | 0.188481 | 0.209612 | 0.083036 | 0.528378 | 0.098395 | 0.270250 | 0.377461 | 0.115948 | -0.070103 | 0.180232 | 0.011502 | 0.123369 | 0.299089 | 0.245980 | 0.219022 | 1.000000 | 0.087156 | -0.000807 | 0.116516 | 0.135430 | 0.082124 | 0.104778 | 0.164511 | -0.000178 | 0.033660 | -0.114746 |
| Functional | 0.006018 | -0.029668 | -0.015465 | -0.017841 | 0.016088 | -0.006303 | 0.008194 | 0.097882 | 0.077679 | -0.003683 | -0.022040 | -0.040279 | 0.036488 | 0.074203 | 0.096702 | 0.062031 | 0.097594 | 0.084426 | 0.098799 | 0.098871 | 0.178060 | 0.079358 | 0.140345 | -0.072731 | 0.130886 | 0.073947 | 0.059807 | 0.059352 | 0.099165 | 0.093060 | 0.087156 | 1.000000 | 0.040767 | 0.090222 | 0.014572 | 0.072965 | 0.042658 | 0.047388 | -0.014494 | -0.055108 | -0.108367 |
| FireplaceQu | -0.024325 | -0.117721 | -0.017230 | 0.102891 | -0.053251 | -0.012962 | 0.057436 | 0.013180 | 0.011046 | -0.012094 | -0.025387 | -0.042824 | -0.016011 | 0.036268 | 0.047445 | 0.104904 | 0.042182 | -0.119612 | -0.017219 | -0.012548 | -0.043634 | -0.054100 | -0.046763 | 0.003477 | -0.075103 | 0.075770 | -0.029643 | -0.009620 | -0.107931 | -0.100106 | -0.000807 | 0.040767 | 1.000000 | -0.094168 | 0.015989 | -0.011604 | -0.044373 | -0.072927 | -0.021735 | -0.081781 | 0.165078 |
| GarageType | 0.009743 | 0.099439 | 0.135136 | -0.045583 | 0.032066 | 0.114822 | -0.006936 | 0.063664 | 0.088574 | 0.021200 | 0.064196 | 0.013266 | 0.112935 | -0.006125 | 0.012190 | 0.020894 | 0.061058 | 0.058091 | 0.135777 | 0.022384 | 0.155160 | 0.096753 | 0.058767 | -0.039763 | 0.102446 | -0.009329 | 0.049661 | 0.064946 | 0.116537 | 0.109852 | 0.116516 | 0.090222 | -0.094168 | 1.000000 | 0.100185 | 0.106045 | 0.084393 | 0.061873 | -0.000543 | 0.043612 | -0.101786 |
| GarageFinish | -0.002604 | 0.005704 | -0.010497 | 0.067334 | 0.055327 | -0.034307 | 0.029645 | 0.018310 | 0.175353 | 0.026413 | 0.008562 | 0.016879 | -0.029816 | 0.072046 | 0.028933 | 0.014384 | 0.013905 | 0.040864 | 0.063070 | 0.003008 | -0.054610 | 0.105632 | 0.024657 | 0.015157 | -0.051446 | -0.030182 | 0.008418 | -0.056186 | 0.017410 | -0.003678 | 0.135430 | 0.014572 | 0.015989 | 0.100185 | 1.000000 | -0.003700 | 0.002641 | 0.015989 | -0.002995 | 0.055764 | 0.141148 |
| GarageQual | -0.014586 | 0.039895 | -0.012414 | -0.046539 | -0.030841 | -0.005059 | 0.006178 | -0.014761 | -0.030853 | 0.006257 | 0.062199 | -0.048735 | 0.073689 | -0.006440 | 0.024703 | 0.043935 | 0.101342 | 0.031918 | 0.031578 | 0.093184 | 0.181056 | 0.142403 | 0.069530 | -0.047842 | 0.044489 | -0.004602 | 0.086007 | 0.054182 | 0.150958 | 0.138483 | 0.082124 | 0.072965 | -0.011604 | 0.106045 | -0.003700 | 1.000000 | 0.614519 | 0.120247 | -0.041587 | -0.006622 | -0.055261 |
| GarageCond | 0.000987 | 0.028618 | -0.010851 | -0.065380 | -0.022491 | -0.004422 | 0.034839 | -0.008288 | 0.006989 | 0.002639 | 0.006563 | -0.056821 | 0.065567 | 0.050651 | 0.059823 | 0.051225 | 0.085423 | 0.033172 | 0.053477 | 0.059977 | 0.144817 | 0.140402 | 0.165657 | -0.033896 | 0.045777 | 0.025548 | 0.133256 | 0.090005 | 0.180947 | 0.163950 | 0.104778 | 0.042658 | -0.044373 | 0.084393 | 0.002641 | 0.614519 | 1.000000 | 0.172531 | -0.021168 | -0.013230 | -0.111891 |
| PavedDrive | 0.008921 | 0.117402 | 0.011248 | -0.100666 | 0.081031 | -0.007368 | -0.065917 | 0.018009 | 0.065998 | 0.034886 | 0.054484 | -0.064447 | 0.038707 | 0.011743 | -0.033947 | 0.058754 | 0.119255 | 0.082059 | 0.166263 | 0.139432 | 0.243846 | 0.143237 | 0.116541 | -0.070336 | 0.114083 | -0.077327 | 0.141864 | 0.193215 | 0.275660 | 0.175431 | 0.164511 | 0.047388 | -0.072927 | 0.061873 | 0.015989 | 0.120247 | 0.172531 | 1.000000 | -0.021553 | -0.049674 | -0.208954 |
| SaleType | -0.016932 | 0.058948 | 0.021171 | 0.001435 | -0.014537 | 0.057455 | 0.012120 | -0.056680 | 0.008466 | 0.003128 | 0.011960 | 0.044289 | -0.006154 | -0.031824 | -0.014712 | -0.015554 | -0.033452 | 0.071044 | 0.022013 | 0.003909 | -0.050491 | 0.029998 | 0.060624 | 0.043161 | -0.017829 | -0.008694 | 0.018388 | -0.009051 | 0.026522 | -0.015363 | -0.000178 | -0.014494 | -0.021735 | -0.000543 | -0.002995 | -0.041587 | -0.021168 | -0.021553 | 1.000000 | 0.232149 | 0.072896 |
| SaleCondition | -0.017712 | 0.068705 | 0.022919 | -0.004773 | 0.062826 | 0.019173 | -0.013638 | 0.017187 | 0.007101 | -0.019800 | 0.005158 | 0.040365 | -0.029049 | 0.032990 | 0.055436 | 0.000631 | -0.039999 | 0.106159 | -0.009670 | -0.038491 | -0.093201 | 0.057392 | -0.004425 | 0.094537 | -0.067683 | -0.060118 | -0.032650 | -0.049721 | -0.010168 | -0.022997 | 0.033660 | -0.055108 | -0.081781 | 0.043612 | 0.055764 | -0.006622 | -0.013230 | -0.049674 | 0.232149 | 1.000000 | 0.142503 |
| SalePrice | -0.021917 | -0.116047 | -0.041036 | 0.267759 | 0.092009 | -0.014314 | 0.109106 | 0.051152 | -0.143621 | -0.044820 | -0.004833 | -0.112611 | -0.188688 | 0.159332 | 0.035820 | -0.120586 | -0.164716 | -0.010695 | -0.265015 | -0.121706 | -0.429678 | -0.004053 | -0.082016 | 0.229632 | -0.360407 | -0.046649 | -0.106673 | -0.427649 | -0.251328 | -0.231417 | -0.114746 | -0.108367 | 0.165078 | -0.101786 | 0.141148 | -0.055261 | -0.111891 | -0.208954 | 0.072896 | 0.142503 | 1.000000 |
sns.set(rc={'figure.figsize':(30,30)})
color = plt.get_cmap('summer')
color.set_bad('lightblue')
sns.heatmap(data=df_c.corr(), square=True, annot=True, fmt='.2g', cmap= color)
<AxesSubplot: >
df_c_high = df_c.loc[:, df_c.corr().abs()['SalePrice'] > 0.3]
df_c_high['Id'] = df_c['Id']
first_column = df_c_high.pop('Id')
df_c_high.insert(0, 'Id', first_column)
df_c_high.head()
| Id | Foundation | BsmtFinType1 | HeatingQC | SalePrice | |
|---|---|---|---|---|---|
| 0 | 1 | 1 | 1 | 1 | 208500 |
| 1 | 2 | 2 | 2 | 1 | 181500 |
| 2 | 3 | 1 | 1 | 1 | 223500 |
| 3 | 4 | 3 | 2 | 2 | 140000 |
| 4 | 5 | 1 | 1 | 1 | 250000 |
sns.set(rc={'figure.figsize':(30,30)})
color = plt.get_cmap('summer')
color.set_bad('lightblue')
sns.heatmap(data=df_c_high.corr(), square=True, annot=True, fmt='.2g', cmap= color)
<AxesSubplot: >
models = ["Foundation", "BsmtFinType1", "HeatingQC"]
dfChi = df_c[models].copy()
dfChi
| Foundation | BsmtFinType1 | HeatingQC | |
|---|---|---|---|
| 0 | 1 | 1 | 1 |
| 1 | 2 | 2 | 1 |
| 2 | 1 | 1 | 1 |
| 3 | 3 | 2 | 2 |
| 4 | 1 | 1 | 1 |
| 5 | 4 | 1 | 1 |
| 6 | 1 | 1 | 1 |
| 7 | 2 | 2 | 1 |
| 8 | 3 | 3 | 2 |
| 9 | 3 | 1 | 1 |
| 10 | 2 | 4 | 1 |
| 11 | 1 | 1 | 1 |
| 12 | 2 | 2 | 3 |
| 13 | 1 | 3 | 1 |
| 14 | 2 | 5 | 3 |
| 15 | 3 | 3 | 1 |
| 16 | 2 | 2 | 1 |
| 17 | 5 | 3 | 3 |
| 18 | 1 | 1 | 1 |
| 19 | 2 | 6 | 3 |
| 20 | 1 | 3 | 1 |
| 21 | 1 | 3 | 1 |
| 22 | 1 | 3 | 1 |
| 23 | 1 | 1 | 3 |
| 24 | 2 | 4 | 1 |
| 25 | 1 | 3 | 1 |
| 26 | 2 | 5 | 3 |
| 27 | 1 | 1 | 1 |
| 28 | 2 | 5 | 3 |
| 29 | 3 | 3 | 4 |
| 30 | 3 | 3 | 3 |
| 31 | 2 | 3 | 2 |
| 32 | 1 | 3 | 1 |
| 33 | 2 | 4 | 2 |
| 34 | 1 | 1 | 1 |
| 35 | 1 | 3 | 1 |
| 36 | 1 | 3 | 1 |
| 37 | 2 | 4 | 2 |
| 38 | 2 | 1 | 3 |
| 39 | 1 | 3 | 3 |
| 40 | 2 | 4 | 1 |
| 41 | 2 | 5 | 2 |
| 42 | 2 | 2 | 2 |
| 43 | 2 | 6 | 3 |
| 44 | 2 | 2 | 1 |
| 45 | 1 | 1 | 1 |
| 46 | 1 | 1 | 1 |
| 47 | 1 | 1 | 1 |
| 48 | 3 | 3 | 2 |
| 49 | 2 | 5 | 1 |
| 50 | 1 | 1 | 2 |
| 51 | 1 | 3 | 3 |
| 52 | 2 | 6 | 3 |
| 53 | 2 | 1 | 2 |
| 54 | 2 | 2 | 3 |
| 55 | 2 | 5 | 2 |
| 56 | 1 | 1 | 1 |
| 57 | 1 | 3 | 1 |
| 58 | 1 | 3 | 1 |
| 59 | 2 | 2 | 1 |
| 60 | 1 | 2 | 1 |
| 61 | 3 | 3 | 3 |
| 62 | 1 | 1 | 1 |
| 63 | 3 | 3 | 2 |
| 64 | 1 | 1 | 1 |
| 65 | 1 | 3 | 1 |
| 66 | 2 | 1 | 3 |
| 67 | 1 | 1 | 1 |
| 68 | 2 | 3 | 3 |
| 69 | 2 | 5 | 3 |
| 70 | 2 | 2 | 1 |
| 71 | 2 | 2 | 3 |
| 72 | 1 | 3 | 2 |
| 73 | 2 | 2 | 2 |
| 74 | 2 | 3 | 2 |
| 75 | 2 | 1 | 3 |
| 76 | 2 | 2 | 1 |
| 77 | 2 | 5 | 3 |
| 78 | 2 | 3 | 3 |
| 79 | 1 | 3 | 2 |
| 80 | 2 | 4 | 3 |
| 81 | 1 | 1 | 1 |
| 82 | 1 | 1 | 1 |
| 83 | 2 | 3 | 2 |
| 84 | 1 | 3 | 2 |
| 85 | 2 | 3 | 1 |
| 86 | 1 | 3 | 1 |
| 87 | 1 | 3 | 1 |
| 88 | 2 | 3 | 3 |
| 89 | 1 | 1 | 1 |
| 90 | 5 | 3 | 3 |
| 91 | 2 | 4 | 3 |
| 92 | 3 | 2 | 1 |
| 93 | 3 | 4 | 1 |
| 94 | 1 | 1 | 1 |
| 95 | 1 | 2 | 2 |
| 96 | 1 | 2 | 1 |
| 97 | 2 | 4 | 3 |
| 98 | 3 | 2 | 4 |
| 99 | 2 | 2 | 4 |
| 100 | 1 | 2 | 2 |
| 101 | 2 | 3 | 3 |
| 102 | 5 | 3 | 3 |
| 103 | 1 | 3 | 1 |
| 104 | 1 | 6 | 1 |
| 105 | 1 | 3 | 1 |
| 106 | 3 | 3 | 2 |
| 107 | 2 | 2 | 1 |
| 108 | 2 | 3 | 3 |
| 109 | 2 | 5 | 1 |
| 110 | 2 | 4 | 2 |
| 111 | 1 | 1 | 1 |
| 112 | 1 | 1 | 1 |
| 113 | 2 | 2 | 3 |
| 114 | 2 | 2 | 1 |
| 115 | 1 | 1 | 2 |
| 116 | 2 | 6 | 3 |
| 117 | 1 | 3 | 2 |
| 118 | 1 | 1 | 2 |
| 119 | 1 | 3 | 1 |
| 120 | 2 | 2 | 1 |
| 121 | 1 | 3 | 2 |
| 122 | 2 | 5 | 2 |
| 123 | 1 | 1 | 1 |
| 124 | 2 | 3 | 3 |
| 125 | 2 | 1 | 2 |
| 126 | 2 | 2 | 3 |
| 127 | 3 | 6 | 1 |
| 128 | 2 | 5 | 3 |
| 129 | 2 | 4 | 3 |
| 130 | 2 | 4 | 1 |
| 131 | 1 | 1 | 1 |
| 132 | 2 | 4 | 2 |
| 133 | 1 | 1 | 1 |
| 134 | 2 | 4 | 2 |
| 135 | 1 | 3 | 2 |
| 136 | 2 | 5 | 3 |
| 137 | 2 | 3 | 2 |
| 138 | 1 | 1 | 1 |
| 139 | 1 | 1 | 1 |
| 140 | 2 | 2 | 3 |
| 141 | 1 | 1 | 1 |
| 142 | 2 | 4 | 4 |
| 143 | 1 | 1 | 1 |
| 144 | 2 | 4 | 3 |
| 145 | 1 | 3 | 1 |
| 146 | 3 | 5 | 3 |
| 147 | 1 | 3 | 1 |
| 148 | 1 | 1 | 1 |
| 149 | 3 | 3 | 2 |
| 150 | 2 | 5 | 3 |
| 151 | 1 | 1 | 1 |
| 152 | 2 | 4 | 2 |
| 153 | 2 | 5 | 2 |
| 154 | 3 | 3 | 3 |
| 155 | 3 | 3 | 4 |
| 156 | 2 | 3 | 3 |
| 157 | 1 | 3 | 1 |
| 158 | 1 | 1 | 1 |
| 159 | 1 | 1 | 1 |
| 160 | 1 | 5 | 3 |
| 161 | 1 | 1 | 1 |
| 162 | 1 | 5 | 1 |
| 163 | 2 | 3 | 1 |
| 164 | 3 | 6 | 2 |
| 165 | 3 | 2 | 2 |
| 166 | 2 | 6 | 1 |
| 167 | 1 | 1 | 1 |
| 168 | 1 | 3 | 1 |
| 169 | 2 | 3 | 3 |
| 170 | 2 | 4 | 3 |
| 171 | 2 | 5 | 4 |
| 172 | 1 | 1 | 2 |
| 173 | 2 | 2 | 3 |
| 174 | 2 | 2 | 3 |
| 175 | 2 | 2 | 3 |
| 176 | 1 | 1 | 3 |
| 177 | 2 | 2 | 1 |
| 178 | 1 | 1 | 1 |
| 179 | 2 | 3 | 3 |
| 180 | 1 | 1 | 1 |
| 181 | 3 | 6 | 2 |
| 182 | 1 | 3 | 1 |
| 183 | 1 | 3 | 1 |
| 184 | 1 | 3 | 2 |
| 185 | 3 | 3 | 1 |
| 186 | 1 | 1 | 1 |
| 187 | 2 | 3 | 1 |
| 188 | 2 | 1 | 3 |
| 189 | 1 | 1 | 1 |
| 190 | 2 | 4 | 3 |
| 191 | 2 | 2 | 2 |
| 192 | 1 | 1 | 1 |
| 193 | 1 | 3 | 1 |
| 194 | 2 | 2 | 3 |
| 195 | 2 | 2 | 3 |
| 196 | 1 | 1 | 1 |
| 197 | 1 | 1 | 2 |
| 198 | 2 | 3 | 1 |
| 199 | 1 | 1 | 1 |
| 200 | 1 | 3 | 1 |
| 201 | 2 | 2 | 3 |
| 202 | 3 | 6 | 2 |
| 203 | 1 | 1 | 1 |
| 204 | 2 | 6 | 3 |
| 205 | 1 | 3 | 1 |
| 206 | 2 | 3 | 3 |
| 207 | 1 | 2 | 3 |
| 208 | 2 | 1 | 1 |
| 209 | 2 | 4 | 1 |
| 210 | 2 | 4 | 3 |
| 211 | 1 | 1 | 1 |
| 212 | 1 | 1 | 1 |
| 213 | 1 | 2 | 1 |
| 214 | 2 | 1 | 1 |
| 215 | 2 | 5 | 3 |
| 216 | 1 | 1 | 1 |
| 217 | 2 | 3 | 4 |
| 218 | 2 | 5 | 1 |
| 219 | 1 | 1 | 1 |
| 220 | 1 | 3 | 1 |
| 221 | 1 | 3 | 1 |
| 222 | 2 | 2 | 3 |
| 223 | 2 | 2 | 2 |
| 224 | 1 | 1 | 1 |
| 225 | 2 | 3 | 3 |
| 226 | 1 | 1 | 1 |
| 227 | 2 | 4 | 3 |
| 228 | 2 | 2 | 3 |
| 229 | 1 | 1 | 1 |
| 230 | 2 | 3 | 3 |
| 231 | 1 | 1 | 1 |
| 232 | 2 | 3 | 3 |
| 233 | 2 | 6 | 3 |
| 234 | 1 | 1 | 1 |
| 235 | 2 | 2 | 3 |
| 236 | 1 | 1 | 1 |
| 237 | 1 | 5 | 1 |
| 238 | 1 | 3 | 1 |
| 239 | 2 | 6 | 3 |
| 240 | 1 | 1 | 1 |
| 241 | 2 | 2 | 2 |
| 242 | 3 | 3 | 2 |
| 243 | 2 | 3 | 3 |
| 244 | 1 | 1 | 1 |
| 245 | 2 | 1 | 2 |
| 246 | 6 | 3 | 2 |
| 247 | 2 | 3 | 1 |
| 248 | 1 | 3 | 1 |
| 249 | 2 | 4 | 2 |
| 250 | 2 | 3 | 1 |
| 251 | 1 | 1 | 1 |
| 252 | 1 | 3 | 1 |
| 253 | 2 | 6 | 1 |
| 254 | 2 | 4 | 3 |
| 255 | 1 | 3 | 1 |
| 256 | 1 | 4 | 1 |
| 257 | 1 | 1 | 1 |
| 258 | 1 | 1 | 1 |
| 259 | 1 | 3 | 2 |
| 260 | 2 | 4 | 3 |
| 261 | 1 | 3 | 1 |
| 262 | 2 | 2 | 3 |
| 263 | 3 | 6 | 3 |
| 264 | 3 | 3 | 2 |
| 265 | 2 | 1 | 3 |
| 266 | 1 | 1 | 2 |
| 267 | 1 | 6 | 1 |
| 268 | 2 | 2 | 1 |
| 269 | 2 | 5 | 3 |
| 270 | 1 | 3 | 2 |
| 271 | 2 | 6 | 1 |
| 272 | 1 | 1 | 1 |
| 273 | 2 | 4 | 2 |
| 274 | 2 | 2 | 3 |
| 275 | 3 | 3 | 2 |
| 276 | 1 | 3 | 1 |
| 277 | 2 | 6 | 1 |
| 278 | 1 | 3 | 1 |
| 279 | 2 | 5 | 1 |
| 280 | 2 | 1 | 2 |
| 281 | 1 | 1 | 2 |
| 282 | 1 | 1 | 1 |
| 283 | 1 | 3 | 1 |
| 284 | 1 | 1 | 2 |
| 285 | 1 | 3 | 1 |
| 286 | 2 | 4 | 3 |
| 287 | 2 | 5 | 3 |
| 288 | 2 | 5 | 3 |
| 289 | 3 | 3 | 1 |
| 290 | 1 | 3 | 1 |
| 291 | 1 | 4 | 1 |
| 292 | 2 | 6 | 2 |
| 293 | 2 | 2 | 4 |
| 294 | 2 | 1 | 3 |
| 295 | 2 | 1 | 3 |
| 296 | 2 | 5 | 3 |
| 297 | 1 | 5 | 1 |
| 298 | 2 | 2 | 2 |
| 299 | 2 | 3 | 1 |
| 300 | 2 | 5 | 3 |
| 301 | 1 | 1 | 1 |
| 302 | 1 | 3 | 1 |
| 303 | 1 | 2 | 3 |
| 304 | 3 | 3 | 1 |
| 305 | 1 | 1 | 1 |
| 306 | 2 | 2 | 2 |
| 307 | 2 | 3 | 3 |
| 308 | 2 | 5 | 1 |
| 309 | 1 | 1 | 1 |
| 310 | 1 | 2 | 2 |
| 311 | 2 | 2 | 1 |
| 312 | 2 | 4 | 3 |
| 313 | 2 | 2 | 3 |
| 314 | 3 | 6 | 1 |
| 315 | 1 | 1 | 1 |
| 316 | 2 | 1 | 3 |
| 317 | 1 | 3 | 1 |
| 318 | 1 | 1 | 1 |
| 319 | 2 | 1 | 3 |
| 320 | 1 | 3 | 1 |
| 321 | 1 | 1 | 1 |
| 322 | 2 | 6 | 1 |
| 323 | 2 | 2 | 1 |
| 324 | 2 | 3 | 1 |
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| 332 | 1 | 1 | 1 |
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| 1030 | 1 | 3 | 2 |
| 1031 | 3 | 1 | 1 |
| 1032 | 1 | 1 | 1 |
| 1033 | 1 | 1 | 1 |
| 1034 | 1 | 3 | 1 |
| 1035 | 5 | 3 | 1 |
| 1036 | 1 | 1 | 1 |
| 1037 | 1 | 3 | 1 |
| 1038 | 2 | 3 | 3 |
| 1039 | 2 | 1 | 3 |
| 1040 | 2 | 4 | 1 |
| 1041 | 2 | 1 | 2 |
| 1042 | 1 | 2 | 1 |
| 1043 | 1 | 1 | 1 |
| 1044 | 1 | 2 | 3 |
| 1045 | 5 | 3 | 1 |
| 1046 | 1 | 1 | 1 |
| 1047 | 1 | 1 | 1 |
| 1048 | 5 | 3 | 3 |
| 1049 | 2 | 3 | 1 |
| 1050 | 1 | 3 | 1 |
| 1051 | 1 | 3 | 1 |
| 1052 | 2 | 4 | 3 |
| 1053 | 2 | 4 | 1 |
| 1054 | 1 | 1 | 1 |
| 1055 | 2 | 2 | 3 |
| 1056 | 1 | 1 | 1 |
| 1057 | 1 | 1 | 1 |
| 1058 | 1 | 1 | 1 |
| 1059 | 2 | 4 | 3 |
| 1060 | 1 | 1 | 1 |
| 1061 | 2 | 3 | 3 |
| 1062 | 3 | 3 | 3 |
| 1063 | 3 | 5 | 2 |
| 1064 | 2 | 5 | 1 |
| 1065 | 1 | 1 | 1 |
| 1066 | 1 | 3 | 2 |
| 1067 | 2 | 1 | 3 |
| 1068 | 2 | 2 | 2 |
| 1069 | 2 | 2 | 3 |
| 1070 | 2 | 5 | 3 |
| 1071 | 2 | 4 | 1 |
| 1072 | 2 | 3 | 4 |
| 1073 | 2 | 5 | 3 |
| 1074 | 1 | 3 | 1 |
| 1075 | 2 | 5 | 3 |
| 1076 | 2 | 2 | 2 |
| 1077 | 2 | 5 | 1 |
| 1078 | 1 | 1 | 1 |
| 1079 | 1 | 1 | 2 |
| 1080 | 2 | 2 | 1 |
| 1081 | 2 | 2 | 4 |
| 1082 | 1 | 3 | 1 |
| 1083 | 2 | 5 | 3 |
| 1084 | 1 | 2 | 2 |
| 1085 | 1 | 1 | 1 |
| 1086 | 2 | 5 | 3 |
| 1087 | 1 | 3 | 1 |
| 1088 | 1 | 3 | 1 |
| 1089 | 1 | 1 | 1 |
| 1090 | 5 | 3 | 4 |
| 1091 | 1 | 5 | 1 |
| 1092 | 1 | 4 | 4 |
| 1093 | 2 | 1 | 2 |
| 1094 | 2 | 5 | 1 |
| 1095 | 1 | 1 | 2 |
| 1096 | 1 | 3 | 3 |
| 1097 | 2 | 3 | 1 |
| 1098 | 3 | 5 | 3 |
| 1099 | 2 | 2 | 3 |
| 1100 | 2 | 4 | 3 |
| 1101 | 2 | 5 | 3 |
| 1102 | 2 | 4 | 1 |
| 1103 | 2 | 2 | 1 |
| 1104 | 2 | 3 | 3 |
| 1105 | 1 | 1 | 1 |
| 1106 | 1 | 1 | 2 |
| 1107 | 1 | 3 | 1 |
| 1108 | 1 | 3 | 1 |
| 1109 | 1 | 1 | 1 |
| 1110 | 1 | 1 | 2 |
| 1111 | 2 | 2 | 3 |
| 1112 | 2 | 1 | 3 |
| 1113 | 2 | 5 | 2 |
| 1114 | 2 | 4 | 1 |
| 1115 | 1 | 1 | 1 |
| 1116 | 1 | 1 | 1 |
| 1117 | 2 | 5 | 1 |
| 1118 | 2 | 3 | 3 |
| 1119 | 2 | 6 | 3 |
| 1120 | 3 | 3 | 3 |
| 1121 | 1 | 1 | 1 |
| 1122 | 2 | 3 | 1 |
| 1123 | 2 | 3 | 1 |
| 1124 | 1 | 3 | 2 |
| 1125 | 2 | 3 | 3 |
| 1126 | 1 | 3 | 1 |
| 1127 | 1 | 1 | 1 |
| 1128 | 1 | 3 | 1 |
| 1129 | 2 | 1 | 3 |
| 1130 | 3 | 5 | 3 |
| 1131 | 1 | 5 | 3 |
| 1132 | 3 | 3 | 3 |
| 1133 | 1 | 1 | 1 |
| 1134 | 1 | 3 | 2 |
| 1135 | 3 | 3 | 3 |
| 1136 | 2 | 5 | 3 |
| 1137 | 2 | 3 | 2 |
| 1138 | 1 | 2 | 3 |
| 1139 | 3 | 5 | 3 |
| 1140 | 2 | 2 | 3 |
| 1141 | 2 | 2 | 1 |
| 1142 | 1 | 1 | 1 |
| 1143 | 2 | 1 | 3 |
| 1144 | 3 | 5 | 4 |
| 1145 | 3 | 3 | 1 |
| 1146 | 2 | 1 | 3 |
| 1147 | 2 | 4 | 1 |
| 1148 | 1 | 3 | 3 |
| 1149 | 1 | 2 | 1 |
| 1150 | 2 | 3 | 1 |
| 1151 | 2 | 5 | 3 |
| 1152 | 1 | 2 | 1 |
| 1153 | 3 | 2 | 1 |
| 1154 | 2 | 2 | 3 |
| 1155 | 2 | 2 | 3 |
| 1156 | 1 | 2 | 2 |
| 1157 | 1 | 1 | 1 |
| 1158 | 1 | 3 | 1 |
| 1159 | 2 | 2 | 3 |
| 1160 | 2 | 2 | 4 |
| 1161 | 2 | 5 | 1 |
| 1162 | 2 | 5 | 2 |
| 1163 | 2 | 1 | 3 |
| 1164 | 1 | 2 | 1 |
| 1165 | 1 | 3 | 1 |
| 1166 | 1 | 3 | 1 |
| 1167 | 1 | 1 | 1 |
| 1168 | 2 | 4 | 1 |
| 1169 | 1 | 1 | 1 |
| 1170 | 2 | 2 | 3 |
| 1171 | 2 | 2 | 1 |
| 1172 | 1 | 3 | 1 |
| 1173 | 2 | 4 | 3 |
| 1174 | 3 | 4 | 3 |
| 1175 | 1 | 1 | 1 |
| 1176 | 2 | 2 | 3 |
| 1177 | 2 | 4 | 3 |
| 1178 | 3 | 3 | 1 |
| 1179 | 5 | 3 | 2 |
| 1180 | 4 | 6 | 1 |
| 1181 | 1 | 1 | 1 |
| 1182 | 1 | 1 | 1 |
| 1183 | 3 | 4 | 1 |
| 1184 | 2 | 4 | 2 |
| 1185 | 3 | 5 | 2 |
| 1186 | 2 | 5 | 3 |
| 1187 | 1 | 1 | 1 |
| 1188 | 1 | 3 | 1 |
| 1189 | 1 | 3 | 2 |
| 1190 | 2 | 5 | 1 |
| 1191 | 1 | 3 | 1 |
| 1192 | 1 | 3 | 2 |
| 1193 | 1 | 1 | 1 |
| 1194 | 2 | 2 | 3 |
| 1195 | 1 | 3 | 1 |
| 1196 | 1 | 3 | 1 |
| 1197 | 3 | 3 | 1 |
| 1198 | 1 | 3 | 1 |
| 1199 | 2 | 4 | 2 |
| 1200 | 2 | 3 | 2 |
| 1201 | 1 | 3 | 1 |
| 1202 | 3 | 3 | 1 |
| 1203 | 1 | 3 | 1 |
| 1204 | 2 | 2 | 1 |
| 1205 | 2 | 2 | 2 |
| 1206 | 2 | 4 | 3 |
| 1207 | 1 | 1 | 1 |
| 1208 | 2 | 4 | 3 |
| 1209 | 1 | 1 | 1 |
| 1210 | 1 | 3 | 1 |
| 1211 | 4 | 1 | 2 |
| 1212 | 2 | 4 | 3 |
| 1213 | 2 | 1 | 1 |
| 1214 | 2 | 5 | 3 |
| 1215 | 2 | 4 | 3 |
| 1216 | 5 | 3 | 3 |
| 1217 | 1 | 1 | 1 |
| 1218 | 5 | 3 | 2 |
| 1219 | 2 | 3 | 3 |
| 1220 | 2 | 4 | 3 |
| 1221 | 2 | 5 | 1 |
| 1222 | 2 | 4 | 1 |
| 1223 | 2 | 6 | 3 |
| 1224 | 1 | 1 | 1 |
| 1225 | 2 | 1 | 1 |
| 1226 | 1 | 3 | 1 |
| 1227 | 2 | 2 | 1 |
| 1228 | 1 | 1 | 1 |
| 1229 | 2 | 2 | 2 |
| 1230 | 2 | 1 | 1 |
| 1231 | 2 | 2 | 3 |
| 1232 | 5 | 3 | 3 |
| 1233 | 2 | 4 | 4 |
| 1234 | 1 | 3 | 3 |
| 1235 | 3 | 3 | 2 |
| 1236 | 1 | 3 | 1 |
| 1237 | 1 | 3 | 1 |
| 1238 | 1 | 3 | 1 |
| 1239 | 1 | 1 | 1 |
| 1240 | 1 | 1 | 1 |
| 1241 | 1 | 3 | 1 |
| 1242 | 2 | 1 | 3 |
| 1243 | 1 | 1 | 1 |
| 1244 | 1 | 3 | 4 |
| 1245 | 2 | 3 | 1 |
| 1246 | 1 | 3 | 1 |
| 1247 | 2 | 1 | 3 |
| 1248 | 3 | 4 | 4 |
| 1249 | 2 | 5 | 3 |
| 1250 | 2 | 2 | 1 |
| 1251 | 1 | 3 | 1 |
| 1252 | 2 | 5 | 3 |
| 1253 | 2 | 6 | 3 |
| 1254 | 1 | 3 | 1 |
| 1255 | 3 | 6 | 3 |
| 1256 | 1 | 1 | 1 |
| 1257 | 1 | 3 | 1 |
| 1258 | 1 | 1 | 1 |
| 1259 | 2 | 2 | 2 |
| 1260 | 1 | 3 | 1 |
| 1261 | 2 | 4 | 2 |
| 1262 | 2 | 3 | 1 |
| 1263 | 1 | 3 | 1 |
| 1264 | 1 | 1 | 1 |
| 1265 | 1 | 1 | 1 |
| 1266 | 2 | 3 | 3 |
| 1267 | 1 | 3 | 1 |
| 1268 | 2 | 4 | 1 |
| 1269 | 2 | 5 | 3 |
| 1270 | 1 | 1 | 3 |
| 1271 | 2 | 3 | 2 |
| 1272 | 2 | 5 | 3 |
| 1273 | 2 | 2 | 2 |
| 1274 | 1 | 3 | 1 |
| 1275 | 2 | 4 | 2 |
| 1276 | 2 | 5 | 3 |
| 1277 | 2 | 2 | 4 |
| 1278 | 1 | 1 | 1 |
| 1279 | 2 | 3 | 3 |
| 1280 | 1 | 1 | 1 |
| 1281 | 2 | 2 | 3 |
| 1282 | 2 | 6 | 3 |
| 1283 | 2 | 3 | 3 |
| 1284 | 1 | 3 | 1 |
| 1285 | 2 | 3 | 1 |
| 1286 | 2 | 2 | 3 |
| 1287 | 2 | 4 | 4 |
| 1288 | 1 | 1 | 1 |
| 1289 | 1 | 3 | 1 |
| 1290 | 1 | 1 | 3 |
| 1291 | 2 | 2 | 3 |
| 1292 | 6 | 3 | 3 |
| 1293 | 1 | 1 | 3 |
| 1294 | 2 | 4 | 3 |
| 1295 | 2 | 5 | 2 |
| 1296 | 2 | 2 | 2 |
| 1297 | 1 | 1 | 2 |
| 1298 | 1 | 1 | 1 |
| 1299 | 2 | 6 | 1 |
| 1300 | 1 | 1 | 1 |
| 1301 | 2 | 5 | 4 |
| 1302 | 1 | 1 | 1 |
| 1303 | 1 | 3 | 1 |
| 1304 | 1 | 3 | 1 |
| 1305 | 1 | 1 | 1 |
| 1306 | 1 | 3 | 1 |
| 1307 | 1 | 2 | 1 |
| 1308 | 2 | 1 | 1 |
| 1309 | 2 | 1 | 2 |
| 1310 | 1 | 1 | 3 |
| 1311 | 1 | 1 | 1 |
| 1312 | 1 | 1 | 1 |
| 1313 | 1 | 3 | 1 |
| 1314 | 2 | 4 | 1 |
| 1315 | 2 | 2 | 3 |
| 1316 | 1 | 3 | 1 |
| 1317 | 1 | 3 | 1 |
| 1318 | 1 | 3 | 1 |
| 1319 | 1 | 2 | 1 |
| 1320 | 2 | 2 | 2 |
| 1321 | 2 | 3 | 3 |
| 1322 | 1 | 1 | 1 |
| 1323 | 2 | 6 | 2 |
| 1324 | 1 | 3 | 1 |
| 1325 | 3 | 3 | 4 |
| 1326 | 3 | 4 | 1 |
| 1327 | 2 | 2 | 1 |
| 1328 | 3 | 6 | 1 |
| 1329 | 1 | 3 | 2 |
| 1330 | 1 | 3 | 1 |
| 1331 | 2 | 2 | 2 |
| 1332 | 2 | 2 | 1 |
| 1333 | 2 | 3 | 1 |
| 1334 | 2 | 6 | 3 |
| 1335 | 2 | 2 | 3 |
| 1336 | 2 | 3 | 3 |
| 1337 | 2 | 3 | 4 |
| 1338 | 1 | 1 | 1 |
| 1339 | 2 | 4 | 1 |
| 1340 | 2 | 3 | 3 |
| 1341 | 1 | 1 | 1 |
| 1342 | 1 | 3 | 1 |
| 1343 | 3 | 3 | 2 |
| 1344 | 1 | 3 | 1 |
| 1345 | 1 | 2 | 2 |
| 1346 | 2 | 5 | 3 |
| 1347 | 1 | 1 | 1 |
| 1348 | 1 | 1 | 1 |
| 1349 | 3 | 6 | 4 |
| 1350 | 2 | 6 | 3 |
| 1351 | 2 | 4 | 1 |
| 1352 | 3 | 3 | 3 |
| 1353 | 1 | 1 | 1 |
| 1354 | 1 | 1 | 1 |
| 1355 | 2 | 3 | 2 |
| 1356 | 2 | 4 | 3 |
| 1357 | 2 | 1 | 1 |
| 1358 | 1 | 1 | 1 |
| 1359 | 1 | 1 | 1 |
| 1360 | 3 | 3 | 1 |
| 1361 | 1 | 2 | 1 |
| 1362 | 3 | 3 | 2 |
| 1363 | 1 | 3 | 1 |
| 1364 | 1 | 3 | 1 |
| 1365 | 1 | 1 | 1 |
| 1366 | 1 | 1 | 1 |
| 1367 | 1 | 2 | 1 |
| 1368 | 1 | 1 | 1 |
| 1369 | 1 | 5 | 1 |
| 1370 | 1 | 2 | 1 |
| 1371 | 2 | 5 | 2 |
| 1372 | 1 | 1 | 1 |
| 1373 | 1 | 1 | 1 |
| 1374 | 1 | 3 | 1 |
| 1375 | 1 | 3 | 1 |
| 1376 | 3 | 4 | 3 |
| 1377 | 2 | 6 | 1 |
| 1378 | 2 | 5 | 3 |
| 1379 | 1 | 3 | 2 |
| 1380 | 3 | 4 | 3 |
| 1381 | 2 | 5 | 1 |
| 1382 | 3 | 3 | 1 |
| 1383 | 3 | 3 | 1 |
| 1384 | 3 | 4 | 3 |
| 1385 | 3 | 5 | 2 |
| 1386 | 2 | 5 | 3 |
| 1387 | 3 | 4 | 3 |
| 1388 | 1 | 1 | 1 |
| 1389 | 3 | 2 | 1 |
| 1390 | 1 | 1 | 1 |
| 1391 | 2 | 3 | 3 |
| 1392 | 2 | 2 | 3 |
| 1393 | 3 | 3 | 1 |
| 1394 | 1 | 1 | 1 |
| 1395 | 1 | 3 | 1 |
| 1396 | 2 | 5 | 3 |
| 1397 | 3 | 3 | 1 |
| 1398 | 2 | 4 | 2 |
| 1399 | 3 | 5 | 1 |
| 1400 | 3 | 3 | 3 |
| 1401 | 1 | 1 | 1 |
| 1402 | 1 | 3 | 1 |
| 1403 | 1 | 1 | 1 |
| 1404 | 1 | 3 | 3 |
| 1405 | 1 | 1 | 1 |
| 1406 | 2 | 1 | 3 |
| 1407 | 2 | 2 | 1 |
| 1408 | 2 | 3 | 2 |
| 1409 | 2 | 3 | 2 |
| 1410 | 1 | 1 | 1 |
| 1411 | 2 | 5 | 1 |
| 1412 | 5 | 3 | 4 |
| 1413 | 1 | 1 | 1 |
| 1414 | 3 | 3 | 2 |
| 1415 | 1 | 2 | 1 |
| 1416 | 1 | 3 | 2 |
| 1417 | 1 | 1 | 1 |
| 1418 | 2 | 5 | 3 |
| 1419 | 2 | 4 | 2 |
| 1420 | 2 | 2 | 2 |
| 1421 | 2 | 2 | 3 |
| 1422 | 1 | 1 | 1 |
| 1423 | 2 | 3 | 3 |
| 1424 | 2 | 2 | 3 |
| 1425 | 2 | 3 | 1 |
| 1426 | 1 | 1 | 1 |
| 1427 | 2 | 5 | 3 |
| 1428 | 2 | 5 | 3 |
| 1429 | 2 | 5 | 1 |
| 1430 | 1 | 3 | 1 |
| 1431 | 2 | 6 | 3 |
| 1432 | 3 | 3 | 3 |
| 1433 | 1 | 3 | 1 |
| 1434 | 2 | 2 | 4 |
| 1435 | 2 | 3 | 3 |
| 1436 | 1 | 2 | 3 |
| 1437 | 1 | 1 | 1 |
| 1438 | 2 | 1 | 3 |
| 1439 | 2 | 1 | 3 |
| 1440 | 3 | 3 | 4 |
| 1441 | 1 | 1 | 1 |
| 1442 | 1 | 1 | 1 |
| 1443 | 3 | 3 | 4 |
| 1444 | 1 | 3 | 1 |
| 1445 | 2 | 6 | 2 |
| 1446 | 2 | 4 | 3 |
| 1447 | 1 | 1 | 1 |
| 1448 | 2 | 3 | 2 |
| 1449 | 2 | 1 | 1 |
| 1450 | 2 | 3 | 3 |
| 1451 | 1 | 3 | 1 |
| 1452 | 1 | 1 | 2 |
| 1453 | 1 | 3 | 1 |
| 1454 | 1 | 1 | 1 |
| 1455 | 1 | 3 | 1 |
| 1456 | 2 | 2 | 3 |
| 1457 | 6 | 1 | 1 |
| 1458 | 2 | 1 | 2 |
| 1459 | 2 | 5 | 2 |
skewValue = df_c_high.skew(axis=0, numeric_only=True)
print(skewValue)
sns.pairplot(df_c_high)
Id 0.000000 Foundation 1.686723 BsmtFinType1 0.518622 HeatingQC 0.540458 SalePrice 1.882876 dtype: float64
<seaborn.axisgrid.PairGrid at 0x7fcabc3240d0>
df_merge = pd.merge(df2_n, df_c_high, how='outer', on = 'Id')
df_merge.head()
| Id | LotFrontage | OverallQual | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | TotalBsmtSF | 1stFlrSF | 2ndFlrSF | GrLivArea | FullBath | TotRmsAbvGrd | Fireplaces | GarageYrBlt | GarageCars | GarageArea | WoodDeckSF | OpenPorchSF | SalePrice_x | Foundation | BsmtFinType1 | HeatingQC | SalePrice_y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 65.0 | 7 | 2003 | 2003 | 196.0 | 706 | 856 | 856 | 854 | 1710 | 2 | 8 | 0 | 2003.0 | 2 | 548 | 0 | 61 | 208500 | 1 | 1 | 1 | 208500 |
| 1 | 2 | 80.0 | 6 | 1976 | 1976 | 0.0 | 978 | 1262 | 1262 | 0 | 1262 | 2 | 6 | 1 | 1976.0 | 2 | 460 | 298 | 0 | 181500 | 2 | 2 | 1 | 181500 |
| 2 | 3 | 68.0 | 7 | 2001 | 2002 | 162.0 | 486 | 920 | 920 | 866 | 1786 | 2 | 6 | 1 | 2001.0 | 2 | 608 | 0 | 42 | 223500 | 1 | 1 | 1 | 223500 |
| 3 | 4 | 60.0 | 7 | 1915 | 1970 | 0.0 | 216 | 756 | 961 | 756 | 1717 | 1 | 7 | 1 | 1998.0 | 3 | 642 | 0 | 35 | 140000 | 3 | 2 | 2 | 140000 |
| 4 | 5 | 84.0 | 8 | 2000 | 2000 | 350.0 | 655 | 1145 | 1145 | 1053 | 2198 | 2 | 9 | 1 | 2000.0 | 3 | 836 | 192 | 84 | 250000 | 1 | 1 | 1 | 250000 |
df_merge.shape
(1460, 24)
df_merge.corr()
| Id | LotFrontage | OverallQual | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | TotalBsmtSF | 1stFlrSF | 2ndFlrSF | GrLivArea | FullBath | TotRmsAbvGrd | Fireplaces | GarageYrBlt | GarageCars | GarageArea | WoodDeckSF | OpenPorchSF | SalePrice_x | Foundation | BsmtFinType1 | HeatingQC | SalePrice_y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Id | 1.000000 | -0.028557 | -0.029109 | -0.012084 | -0.021998 | -0.041853 | -0.013375 | -0.023009 | 0.005143 | 0.005193 | 0.002683 | 0.005587 | 0.021054 | -0.018479 | -0.000081 | 0.013672 | 0.013392 | -0.033566 | -0.011482 | -0.027439 | 0.020023 | 0.012783 | 0.015001 | -0.021917 |
| LotFrontage | -0.028557 | 1.000000 | 0.243394 | 0.141219 | 0.076761 | 0.212129 | 0.146139 | 0.336165 | 0.392400 | 0.069453 | 0.341649 | 0.192218 | 0.336975 | 0.227339 | 0.078353 | 0.311437 | 0.345168 | 0.104215 | 0.147604 | 0.376300 | -0.097487 | -0.053252 | -0.081754 | 0.371558 |
| OverallQual | -0.029109 | 0.243394 | 1.000000 | 0.575160 | 0.550829 | 0.418265 | 0.229718 | 0.541432 | 0.473935 | 0.294860 | 0.597218 | 0.550501 | 0.435768 | 0.396809 | 0.519893 | 0.609159 | 0.568185 | 0.246423 | 0.358426 | 0.817680 | -0.477282 | -0.375150 | -0.457410 | 0.791965 |
| YearBuilt | -0.012084 | 0.141219 | 0.575160 | 1.000000 | 0.594909 | 0.350369 | 0.252814 | 0.410450 | 0.288419 | 0.011488 | 0.214644 | 0.469824 | 0.106356 | 0.148330 | 0.782454 | 0.546705 | 0.487446 | 0.238377 | 0.262130 | 0.570327 | -0.648157 | -0.448995 | -0.449252 | 0.524172 |
| YearRemodAdd | -0.021998 | 0.076761 | 0.550829 | 0.594909 | 1.000000 | 0.188731 | 0.126337 | 0.301581 | 0.246662 | 0.140013 | 0.296849 | 0.439046 | 0.196675 | 0.110941 | 0.618391 | 0.425546 | 0.377335 | 0.222702 | 0.280916 | 0.552061 | -0.477886 | -0.399609 | -0.550017 | 0.507101 |
| MasVnrArea | -0.041853 | 0.212129 | 0.418265 | 0.350369 | 0.188731 | 1.000000 | 0.250482 | 0.358988 | 0.339379 | 0.143741 | 0.363829 | 0.276848 | 0.281925 | 0.252411 | 0.269059 | 0.388521 | 0.386561 | 0.161049 | 0.176371 | 0.454043 | -0.222327 | -0.184680 | -0.160608 | 0.452127 |
| BsmtFinSF1 | -0.013375 | 0.146139 | 0.229718 | 0.252814 | 0.126337 | 0.250482 | 1.000000 | 0.467348 | 0.395381 | -0.157621 | 0.138386 | 0.052875 | 0.017356 | 0.243934 | 0.148649 | 0.231241 | 0.278344 | 0.209158 | 0.088825 | 0.387583 | -0.204704 | -0.328471 | -0.082747 | 0.400319 |
| TotalBsmtSF | -0.023009 | 0.336165 | 0.541432 | 0.410450 | 0.301581 | 0.358988 | 0.467348 | 1.000000 | 0.807158 | -0.206180 | 0.404359 | 0.331598 | 0.271000 | 0.328147 | 0.322837 | 0.461365 | 0.485658 | 0.241778 | 0.249657 | 0.645250 | -0.420562 | -0.222359 | -0.274791 | 0.636999 |
| 1stFlrSF | 0.005143 | 0.392400 | 0.473935 | 0.288419 | 0.246662 | 0.339379 | 0.395381 | 0.807158 | 1.000000 | -0.227040 | 0.526708 | 0.383459 | 0.401270 | 0.404117 | 0.230210 | 0.459572 | 0.487425 | 0.238082 | 0.204385 | 0.621874 | -0.189160 | -0.181097 | -0.195179 | 0.620740 |
| 2ndFlrSF | 0.005193 | 0.069453 | 0.294860 | 0.011488 | 0.140013 | 0.143741 | -0.157621 | -0.206180 | -0.227040 | 1.000000 | 0.691087 | 0.420874 | 0.610949 | 0.195192 | 0.069278 | 0.183574 | 0.141130 | 0.089820 | 0.219093 | 0.316508 | -0.071301 | -0.019549 | -0.141724 | 0.316547 |
| GrLivArea | 0.002683 | 0.341649 | 0.597218 | 0.214644 | 0.296849 | 0.363829 | 0.138386 | 0.404359 | 0.526708 | 0.691087 | 1.000000 | 0.641858 | 0.835194 | 0.462469 | 0.227572 | 0.487803 | 0.471376 | 0.243034 | 0.347630 | 0.729315 | -0.198735 | -0.144240 | -0.260799 | 0.708136 |
| FullBath | 0.005587 | 0.192218 | 0.550501 | 0.469824 | 0.439046 | 0.276848 | 0.052875 | 0.331598 | 0.383459 | 0.420874 | 0.641858 | 1.000000 | 0.556469 | 0.245253 | 0.468694 | 0.476141 | 0.414915 | 0.200786 | 0.299482 | 0.583994 | -0.318128 | -0.271030 | -0.333499 | 0.560664 |
| TotRmsAbvGrd | 0.021054 | 0.336975 | 0.435768 | 0.106356 | 0.196675 | 0.281925 | 0.017356 | 0.271000 | 0.401270 | 0.610949 | 0.835194 | 0.556469 | 1.000000 | 0.329225 | 0.142458 | 0.372320 | 0.339538 | 0.166798 | 0.252140 | 0.539339 | -0.101301 | -0.061963 | -0.170769 | 0.536067 |
| Fireplaces | -0.018479 | 0.227339 | 0.396809 | 0.148330 | 0.110941 | 0.252411 | 0.243934 | 0.328147 | 0.404117 | 0.195192 | 0.462469 | 0.245253 | 0.329225 | 1.000000 | 0.045929 | 0.308721 | 0.268397 | 0.201902 | 0.174771 | 0.488848 | -0.124461 | -0.080684 | -0.130323 | 0.469543 |
| GarageYrBlt | -0.000081 | 0.078353 | 0.519893 | 0.782454 | 0.618391 | 0.269059 | 0.148649 | 0.322837 | 0.230210 | 0.069278 | 0.227572 | 0.468694 | 0.142458 | 0.045929 | 1.000000 | 0.489006 | 0.482098 | 0.236382 | 0.278921 | 0.503068 | -0.566991 | -0.435907 | -0.457660 | 0.470269 |
| GarageCars | 0.013672 | 0.311437 | 0.609159 | 0.546705 | 0.425546 | 0.388521 | 0.231241 | 0.461365 | 0.459572 | 0.183574 | 0.487803 | 0.476141 | 0.372320 | 0.308721 | 0.489006 | 1.000000 | 0.895027 | 0.233857 | 0.265562 | 0.678508 | -0.376198 | -0.314252 | -0.330534 | 0.646652 |
| GarageArea | 0.013392 | 0.345168 | 0.568185 | 0.487446 | 0.377335 | 0.386561 | 0.278344 | 0.485658 | 0.487425 | 0.141130 | 0.471376 | 0.414915 | 0.339538 | 0.268397 | 0.482098 | 0.895027 | 1.000000 | 0.232125 | 0.277130 | 0.660029 | -0.335340 | -0.289625 | -0.298013 | 0.630135 |
| WoodDeckSF | -0.033566 | 0.104215 | 0.246423 | 0.238377 | 0.222702 | 0.161049 | 0.209158 | 0.241778 | 0.238082 | 0.089820 | 0.243034 | 0.200786 | 0.166798 | 0.201902 | 0.236382 | 0.233857 | 0.232125 | 1.000000 | 0.081119 | 0.343660 | -0.192402 | -0.172493 | -0.141086 | 0.330378 |
| OpenPorchSF | -0.011482 | 0.147604 | 0.358426 | 0.262130 | 0.280916 | 0.176371 | 0.088825 | 0.249657 | 0.204385 | 0.219093 | 0.347630 | 0.299482 | 0.252140 | 0.174771 | 0.278921 | 0.265562 | 0.277130 | 0.081119 | 1.000000 | 0.394637 | -0.253742 | -0.166635 | -0.229056 | 0.369024 |
| SalePrice_x | -0.027439 | 0.376300 | 0.817680 | 0.570327 | 0.552061 | 0.454043 | 0.387583 | 0.645250 | 0.621874 | 0.316508 | 0.729315 | 0.583994 | 0.539339 | 0.488848 | 0.503068 | 0.678508 | 0.660029 | 0.343660 | 0.394637 | 1.000000 | -0.466808 | -0.378836 | -0.460160 | 0.955438 |
| Foundation | 0.020023 | -0.097487 | -0.477282 | -0.648157 | -0.477886 | -0.222327 | -0.204704 | -0.420562 | -0.189160 | -0.071301 | -0.198735 | -0.318128 | -0.101301 | -0.124461 | -0.566991 | -0.376198 | -0.335340 | -0.192402 | -0.253742 | -0.466808 | 1.000000 | 0.329651 | 0.419451 | -0.429678 |
| BsmtFinType1 | 0.012783 | -0.053252 | -0.375150 | -0.448995 | -0.399609 | -0.184680 | -0.328471 | -0.222359 | -0.181097 | -0.019549 | -0.144240 | -0.271030 | -0.061963 | -0.080684 | -0.435907 | -0.314252 | -0.289625 | -0.172493 | -0.166635 | -0.378836 | 0.329651 | 1.000000 | 0.303916 | -0.360407 |
| HeatingQC | 0.015001 | -0.081754 | -0.457410 | -0.449252 | -0.550017 | -0.160608 | -0.082747 | -0.274791 | -0.195179 | -0.141724 | -0.260799 | -0.333499 | -0.170769 | -0.130323 | -0.457660 | -0.330534 | -0.298013 | -0.141086 | -0.229056 | -0.460160 | 0.419451 | 0.303916 | 1.000000 | -0.427649 |
| SalePrice_y | -0.021917 | 0.371558 | 0.791965 | 0.524172 | 0.507101 | 0.452127 | 0.400319 | 0.636999 | 0.620740 | 0.316547 | 0.708136 | 0.560664 | 0.536067 | 0.469543 | 0.470269 | 0.646652 | 0.630135 | 0.330378 | 0.369024 | 0.955438 | -0.429678 | -0.360407 | -0.427649 | 1.000000 |
sns.set(rc={'figure.figsize':(30,30)})
color = plt.get_cmap('summer')
color.set_bad('lightblue')
sns.heatmap(data=df_merge.corr(), square=True, annot=True, fmt='.2g', cmap= color)
<AxesSubplot: >
df_final = df_merge
df_final.head()
| Id | LotFrontage | OverallQual | YearBuilt | YearRemodAdd | MasVnrArea | BsmtFinSF1 | TotalBsmtSF | 1stFlrSF | 2ndFlrSF | GrLivArea | FullBath | TotRmsAbvGrd | Fireplaces | GarageYrBlt | GarageCars | GarageArea | WoodDeckSF | OpenPorchSF | SalePrice_x | Foundation | BsmtFinType1 | HeatingQC | SalePrice_y | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 65.0 | 7 | 2003 | 2003 | 196.0 | 706 | 856 | 856 | 854 | 1710 | 2 | 8 | 0 | 2003.0 | 2 | 548 | 0 | 61 | 208500 | 1 | 1 | 1 | 208500 |
| 1 | 2 | 80.0 | 6 | 1976 | 1976 | 0.0 | 978 | 1262 | 1262 | 0 | 1262 | 2 | 6 | 1 | 1976.0 | 2 | 460 | 298 | 0 | 181500 | 2 | 2 | 1 | 181500 |
| 2 | 3 | 68.0 | 7 | 2001 | 2002 | 162.0 | 486 | 920 | 920 | 866 | 1786 | 2 | 6 | 1 | 2001.0 | 2 | 608 | 0 | 42 | 223500 | 1 | 1 | 1 | 223500 |
| 3 | 4 | 60.0 | 7 | 1915 | 1970 | 0.0 | 216 | 756 | 961 | 756 | 1717 | 1 | 7 | 1 | 1998.0 | 3 | 642 | 0 | 35 | 140000 | 3 | 2 | 2 | 140000 |
| 4 | 5 | 84.0 | 8 | 2000 | 2000 | 350.0 | 655 | 1145 | 1145 | 1053 | 2198 | 2 | 9 | 1 | 2000.0 | 3 | 836 | 192 | 84 | 250000 | 1 | 1 | 1 | 250000 |